Initial Commit

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+data/processed/eval1.csv filter=lfs diff=lfs merge=lfs -text
+docs/model_score_ranking_exec.png filter=lfs diff=lfs merge=lfs -text
+docs/model_score_ranking.png filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,39 @@
+FROM python:3.11-slim
+
+# -------------------------
+# 1. System deps (for torch, lightgbm, numpy / scipy stack)
+# -------------------------
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    libopenblas-dev \
+    libomp-dev \
+    git \
+    curl \
+    ca-certificates \
+    && rm -rf /var/lib/apt/lists/*
+
+# -------------------------
+# 2. Workdir & env
+# -------------------------
+WORKDIR /workspace
+
+ENV PYTHONUNBUFFERED=1 \
+    PIP_NO_CACHE_DIR=1
+
+# -------------------------
+# 3. Install Python deps
+# -------------------------
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# -------------------------
+# 4. Copy project
+# -------------------------
+COPY . .
+
+# -------------------------
+# 5. Streamlit config
+# -------------------------
+EXPOSE 7860
+
+CMD ["streamlit", "run", "app/app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app/app.py

@@ -0,0 +1,363 @@
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from typing import Optional
+
+import streamlit as st
+import plotly.graph_objects as go
+
+
+# -------------------
+# Paths
+# -------------------
+BASE_DIR = Path(__file__).resolve().parents[1]
+
+DATA_DIR = BASE_DIR / "data" / "processed"
+METRICS_DIR = BASE_DIR / "metrics"
+
+TEST_PATH = DATA_DIR / "test.csv"
+BEST_MODELS_PATH = METRICS_DIR / "best_models.csv"
+COMBINED_METRICS_PATH = METRICS_DIR / "combined_metrics.csv"
+BASELINE_PRED_PATH = METRICS_DIR / "baseline_predictions.csv"
+LGBM_PRED_PATH = METRICS_DIR / "lgbm_predictions.csv"
+CHRONOS_PRED_PATH = METRICS_DIR / "chronos_predictions.csv"
+DEMAND_PROFILE_PATH = METRICS_DIR / "demand_profile.csv"  # ADI / CV2
+BEST_MODEL_OVERALL_PATH = METRICS_DIR / "best_model_overall.csv"
+
+
+# -------------------
+# Cached loaders
+# -------------------
+@st.cache_data
+def load_test() -> pd.DataFrame:
+    df = pd.read_csv(TEST_PATH)
+    # ensure date sorted & numeric if needed
+    return df.sort_values(["id", "date"]).reset_index(drop=True)
+
+
+@st.cache_data
+def load_best_models() -> pd.DataFrame:
+    return pd.read_csv(BEST_MODELS_PATH)
+
+
+@st.cache_data
+def load_best_model_overall() -> pd.DataFrame:
+    return pd.read_csv(BEST_MODEL_OVERALL_PATH)
+
+
+@st.cache_data
+def load_combined_metrics() -> pd.DataFrame:
+    return pd.read_csv(COMBINED_METRICS_PATH)
+
+
+@st.cache_data
+def load_predictions() -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+    """
+    Baselines: metrics/baseline_predictions.csv
+        columns: id, model, h, forecast
+
+    LightGBM: metrics/lgbm_predictions.csv
+        columns: id, h (optional), forecast (or similar)
+
+    Chronos2: metrics/chronos_predictions.csv
+        columns: id, h, q10/q50/q90 or 0.1/0.5/0.9 etc.
+    """
+    # Baseline family (classical / Croston / theta / etc.)
+    df_base = pd.read_csv(BASELINE_PRED_PATH)
+
+    # LightGBM
+    df_lgbm = pd.read_csv(LGBM_PRED_PATH)
+    df_lgbm["model"] = "lightgbm"
+
+    # Chronos2
+    df_chronos = pd.read_csv(CHRONOS_PRED_PATH)
+
+    # Normalize Chronos forecast column → 'forecast'
+    if "q50" in df_chronos.columns:
+        df_chronos = df_chronos.rename(columns={"q50": "forecast"})
+    elif "0.5" in df_chronos.columns:
+        df_chronos = df_chronos.rename(columns={"0.5": "forecast"})
+    elif "predictions" in df_chronos.columns:
+        df_chronos = df_chronos.rename(columns={"predictions": "forecast"})
+
+    # Ensure an 'h' column exists for horizon ordering
+    if "h" not in df_chronos.columns:
+        # if no explicit horizon, infer by group order
+        df_chronos["h"] = df_chronos.groupby("id").cumcount() + 1
+
+    return df_base, df_lgbm, df_chronos
+
+
+@st.cache_data
+def load_demand_profile() -> Optional[pd.DataFrame]:
+    if DEMAND_PROFILE_PATH.exists():
+        return pd.read_csv(DEMAND_PROFILE_PATH)
+    return None
+
+
+# -------------------
+# Helper: align predictions to test dates
+# -------------------
+def align_with_test_dates(
+    test_df: pd.DataFrame,
+    pred_df: pd.DataFrame,
+    sku_id: str,
+    model_name: Optional[str] = None,
+    horizon_col: str = "h",
+) -> pd.DataFrame:
+    """
+    Align predictions for a given SKU (and optional model) to the dates in test_df.
+
+    Logic:
+    - Take all test rows for this SKU and sort by 'date'.
+    - Take all prediction rows for this SKU (and model, if given).
+    - For baselines/Chronos2: sort by horizon_col (e.g. 'h').
+      For LightGBM: sort by existing 'date' or index (we ignore its date values).
+    - Overwrite/add a 'date' column in predictions using the test dates (by position).
+    """
+    # 1) Test horizon for this SKU
+    sku_test = test_df[test_df["id"] == sku_id].sort_values("date")
+    dates = sku_test["date"].values
+
+    # 2) Filter predictions
+    sku_pred = pred_df.copy()
+    if "id" in sku_pred.columns:
+        sku_pred = sku_pred[sku_pred["id"] == sku_id].copy()
+    if model_name is not None and "model" in sku_pred.columns:
+        sku_pred = sku_pred[sku_pred["model"] == model_name].copy()
+
+    if sku_pred.empty:
+        return sku_pred
+
+    # 3) Sort predictions by available structure
+    if horizon_col in sku_pred.columns:
+        # baselines / Chronos: use horizon 'h'
+        sku_pred = sku_pred.sort_values(horizon_col)
+    else:
+        # LightGBM: ignore whatever 'date' means, just use row order
+        if "date" in sku_pred.columns:
+            sku_pred = sku_pred.sort_values("date")
+        else:
+            sku_pred = sku_pred.sort_index()
+
+    sku_pred = sku_pred.reset_index(drop=True)
+
+    # 4) Map dates 1:1 by position
+    n = min(len(dates), len(sku_pred))
+    sku_pred = sku_pred.iloc[:n].copy()
+    sku_pred["date"] = dates[:n]
+
+    return sku_pred
+
+
+# -------------------
+# Helper: classify regime (for display)
+# -------------------
+def classify_regime(row, adi_thr: float = 1.32, cv2_thr: float = 0.49) -> str:
+    adi_class = "High" if row["ADI"] > adi_thr else "Low"
+    cv2_class = "High" if row["CV2"] > cv2_thr else "Low"
+
+    if adi_class == "Low" and cv2_class == "Low":
+        return "Low-Low (Smooth)"
+    if adi_class == "Low" and cv2_class == "High":
+        return "Low-High (Erratic)"
+    if adi_class == "High" and cv2_class == "Low":
+        return "High-Low (Intermittent)"
+    return "High-High (Lumpy)"
+
+
+# -------------------
+# Main app
+# -------------------
+def main() -> None:
+    st.set_page_config(
+        page_title="Forecast Sandbox Lite",
+        layout="wide",
+    )
+
+    st.title("Forecast Sandbox Lite — SKU Explorer")
+    st.caption("Interactive view of model selection, regime profile, and forecast vs actuals.")
+
+    # ---- load core data ----
+    test_df = load_test()
+    best_df = load_best_models()
+    best_model_overall = load_best_model_overall()
+    metrics_df = load_combined_metrics()
+    df_base, df_lgbm, df_chronos = load_predictions()
+    demand_prof = load_demand_profile()
+
+    skus = sorted(test_df["id"].unique())
+    selected_sku = st.selectbox("Select SKU", skus)
+
+    # ---- left: summary info ----
+    col_info, col_plot = st.columns([1, 2])
+
+    with col_info:
+        st.subheader("SKU Summary")
+
+        # best model
+        row_best = best_df[best_df["id"] == selected_sku]
+        if row_best.empty:
+            st.error("No best model found for this SKU.")
+            return
+
+        best_model = row_best["best_model"].iloc[0]
+        st.markdown(f"**Recommended Model:** `{best_model}`")
+
+        # metrics for this SKU
+        sku_metrics = (
+            metrics_df[metrics_df["id"] == selected_sku]
+            .sort_values("score")
+            .reset_index(drop=True)
+        )
+        best_row_metric = sku_metrics[sku_metrics["model"] == best_model].iloc[0]
+
+        st.markdown("**Model Performance (Score = MAE + |Bias|)**")
+        st.write(
+            {
+                "Score": round(best_row_metric["score"], 3),
+                "MAE": round(best_row_metric["mae"], 3),
+                "Bias": round(best_row_metric["bias"], 3),
+            }
+        )
+
+        # regime info (ADI / CV2) if available
+        if (
+            demand_prof is not None
+            and "ADI" in demand_prof.columns
+            and "CV2" in demand_prof.columns
+        ):
+            row_prof = demand_prof[demand_prof["id"] == selected_sku]
+            if not row_prof.empty:
+                row_prof = row_prof.iloc[0]
+                regime_label = classify_regime(row_prof)
+                st.markdown("**Demand Regime (ADI–CV²):**")
+                st.write(
+                    {
+                        "ADI": round(row_prof["ADI"], 2),
+                        "CV²": round(row_prof["CV2"], 2),
+                        "Regime": regime_label,
+                    }
+                )
+
+        st.markdown("---")
+        st.markdown("**All Models for This SKU**")
+        st.dataframe(
+            sku_metrics[["model", "mae", "bias", "score"]],
+            use_container_width=True,
+            height=300,
+        )
+
+    # ---- right: plot ----
+    with col_plot:
+        st.subheader("Actual vs Forecast")
+
+        sku_test = test_df[test_df["id"] == selected_sku].sort_values("date")
+
+        # ---- align predictions with test dates ----
+        if best_model == "lightgbm":
+            raw_pred = df_lgbm
+            sku_pred = align_with_test_dates(
+                test_df=test_df,
+                pred_df=raw_pred,
+                sku_id=selected_sku,
+                model_name=None,   # df_lgbm already only has lightgbm
+                horizon_col="h",   # will be ignored if missing
+            )
+        elif best_model == "chronos2":
+            raw_pred = df_chronos
+            sku_pred = align_with_test_dates(
+                test_df=test_df,
+                pred_df=raw_pred,
+                sku_id=selected_sku,
+                model_name=None,   # chronos df keyed only by id + h
+                horizon_col="h",
+            )
+        else:
+            # Baseline predictions for this SKU & best model
+            raw_pred = df_base
+            sku_pred = align_with_test_dates(
+                test_df=test_df,
+                pred_df=raw_pred,
+                sku_id=selected_sku,
+                model_name=best_model,
+                horizon_col="h",
+            )
+
+        if sku_pred.empty:
+            st.error("No predictions found for this SKU/model combination.")
+            return
+
+        # ensure a 'forecast' column exists
+        if "forecast" not in sku_pred.columns:
+            for cand in ["y_pred", "prediction", "pred", "yhat"]:
+                if cand in sku_pred.columns:
+                    sku_pred = sku_pred.rename(columns={cand: "forecast"})
+                    break
+
+        if "forecast" not in sku_pred.columns:
+            st.error("Predictions for this SKU do not contain a 'forecast' column.")
+            return
+
+        sku_pred = sku_pred.sort_values("date")
+
+        # merge actual + forecast on aligned 'date'
+        merged = sku_test.merge(
+            sku_pred[["date", "forecast"]],
+            on="date",
+            how="left",
+        )
+
+        fig = go.Figure()
+        fig.add_trace(
+            go.Scatter(
+                x=merged["date"],
+                y=merged["sales"],
+                mode="lines",
+                name="Actual",
+            )
+        )
+        fig.add_trace(
+            go.Scatter(
+                x=merged["date"],
+                y=merged["forecast"],
+                mode="lines+markers",
+                name=f"Forecast ({best_model})",
+            )
+        )
+
+        fig.update_layout(
+            xaxis_title="Date",
+            yaxis_title="Sales",
+            template="plotly_white",
+            legend=dict(
+                orientation="h",
+                yanchor="bottom",
+                y=1.02,
+                xanchor="right",
+                x=1,
+            ),
+        )
+
+        st.plotly_chart(fig, use_container_width=True)
+
+        # download section
+        st.markdown("### Download Forecast Data")
+        csv = merged.to_csv(index=False).encode("utf-8")
+        st.download_button(
+            "Download CSV for this SKU",
+            data=csv,
+            file_name=f"{selected_sku}_forecast_vs_actual.csv",
+            mime="text/csv",
+        )
+
+        st.markdown("**Best Overall Model (Lower Score is better)**")
+        st.dataframe(
+            best_model_overall[["model", "score"]],
+            use_container_width=True,
+            height=300,
+        )
+
+
+if __name__ == "__main__":
+    main()

data/prepare_data_fresh.ipynb

@@ -0,0 +1,567 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "8135b7cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# 1. Load HF dataset and convert to pandas\n",
+    "raw_df = pd.read_parquet(\"raw/FreshRetailNet-50K/train.parquet\")\n",
+    "df_fresh_eval = pd.read_parquet(\"raw/FreshRetailNet-50K/eval.parquet\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "8c97255d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# raw_df = raw_df.head(2000)\n",
+    "# 2. Rename for cleanliness\n",
+    "df = raw_df.rename(columns={\n",
+    "    \"dt\": \"date\",\n",
+    "    \"sale_amount\": \"sales\",\n",
+    "    \"first_category_id\": \"category_1\",\n",
+    "    \"second_category_id\": \"category_2\",\n",
+    "    \"third_category_id\": \"category_3\",\n",
+    "    \"holiday_flag\": \"holiday\",\n",
+    "    \"precpt\": \"precip\",\n",
+    "    \"avg_temperature\": \"temp\",\n",
+    "    \"avg_humidity\": \"humidity\",\n",
+    "    \"avg_wind_level\": \"wind_level\",\n",
+    "})\n",
+    "\n",
+    "df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
+    "\n",
+    "# 3. Build a stable SKU-ID like you did for M5\n",
+    "#    here: city–store–product triple → id\n",
+    "df[\"id\"] = \"CID\" + df[\"city_id\"].astype(str) + \"_SID\" + df[\"store_id\"].astype(str) + \"_PID\" + df[\"product_id\"].astype(str) + \"_MGID\" + \\\n",
+    "    df[\"management_group_id\"].astype(str) + \"_CAT1\" + df[\"category_1\"].astype(str) + \"-CAT2\" + df[\"category_2\"].astype(str) + \"-CAT3\" + df[\"category_3\"].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "92e0a3e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def extract_daily_features(row):\n",
+    "    hs = np.array(row[\"hours_sale\"], dtype=float)        # length 24\n",
+    "    st = np.array(row[\"hours_stock_status\"], dtype=int)  # 1 = out-of-stock\n",
+    "\n",
+    "    sale_hours = (hs > 0).sum()\n",
+    "    sale_hour_ratio = sale_hours / 24.0\n",
+    "\n",
+    "    stockout_hours = st.sum()\n",
+    "    stockout_hour_ratio = stockout_hours / 24.0\n",
+    "    avail_hour_ratio = 1.0 - stockout_hour_ratio\n",
+    "    \n",
+    "    return pd.Series({\n",
+    "        \"sale_hours\": sale_hours,\n",
+    "        \"sale_hour_ratio\": sale_hour_ratio,\n",
+    "        \"stockout_hours\": stockout_hours,\n",
+    "        \"stockout_hour_ratio\": stockout_hour_ratio,\n",
+    "        \"avail_hour_ratio\": avail_hour_ratio,\n",
+    "    })\n",
+    "\n",
+    "daily_feats = df.apply(extract_daily_features, axis=1)\n",
+    "df = pd.concat([df, daily_feats], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "63a2b86c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tidy_df = df[['id',\n",
+    "    \"date\",\n",
+    "    \"city_id\", \"store_id\", \"product_id\",\n",
+    "    \"management_group_id\", \"category_1\", \"category_2\", \"category_3\",\n",
+    "    \"sales\",\n",
+    "    \"sale_hours\", \"sale_hour_ratio\",\n",
+    "    \"stockout_hours\", \"stockout_hour_ratio\", \"avail_hour_ratio\",\n",
+    "    \"stock_hour6_22_cnt\",\n",
+    "    \"discount\", \"holiday\", \"activity_flag\",\n",
+    "    \"precip\", \"temp\", \"humidity\", \"wind_level\",\n",
+    "]].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "7ae8449b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = tidy_df.groupby(\"id\")[\"sales\"]\n",
+    "\n",
+    "summary = g.agg([\"mean\", \"std\", \"count\"])\n",
+    "summary = summary.rename(columns={\"count\": \"T\"})\n",
+    "\n",
+    "summary[\"N\"] = g.apply(lambda x: (x > 0).sum())\n",
+    "summary[\"ADI\"] = summary[\"T\"] / summary[\"N\"].replace(0, 1)\n",
+    "summary[\"CV2\"] = (summary[\"std\"] / summary[\"mean\"].replace(0, 1)) ** 2\n",
+    "\n",
+    "summary[\"ADI_class\"] = np.where(summary[\"ADI\"] > 1.32, \"High\", \"Low\")\n",
+    "summary[\"CV2_class\"] = np.where(summary[\"CV2\"] > 0.49, \"High\", \"Low\")\n",
+    "summary[\"regime\"] = summary[\"ADI_class\"] + \"-\" + summary[\"CV2_class\"]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "ee705c5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tidy_df = tidy_df.merge(summary, on=\"id\", how=\"left\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43475b4b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "b394aa1b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "regime\n",
+      "High-High    1800\n",
+      "Low-High      900\n",
+      "Low-Low       900\n",
+      "Name: count, dtype: int64\n",
+      "regime\n",
+      "High-High    0.50\n",
+      "Low-High     0.25\n",
+      "Low-Low      0.25\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\topra\\AppData\\Local\\Temp\\ipykernel_31124\\3370329160.py:25: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "  tidy_subset = pd.concat(\n"
+     ]
+    }
+   ],
+   "source": [
+    "tidy_high_high = tidy_df[tidy_df[\"regime\"] == \"High-High\"]\n",
+    "tidy_low_high  = tidy_df[tidy_df[\"regime\"] == \"Low-High\"]\n",
+    "tidy_high_low   = tidy_df[tidy_df[\"regime\"] == \"High-Low\"]\n",
+    "tidy_low_low   = tidy_df[tidy_df[\"regime\"] == \"Low-Low\"]\n",
+    "\n",
+    "def sample_by_regime(df_regime: pd.DataFrame, num_ids_needed: int) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Sample num_ids_needed unique IDs from df_regime and return all their history.\n",
+    "    \"\"\"\n",
+    "    concat_df = []\n",
+    "    for i, sku_id in enumerate(df_regime[\"id\"].unique()):\n",
+    "        if i < num_ids_needed:\n",
+    "            concat_df.append(df_regime[df_regime[\"id\"] == sku_id])\n",
+    "        else:\n",
+    "            break\n",
+    "    if not concat_df:\n",
+    "        return pd.DataFrame(columns=df_regime.columns)\n",
+    "    return pd.concat(concat_df, ignore_index=True)\n",
+    "\n",
+    "df_high_high_sampled = sample_by_regime(tidy_high_high, 20)\n",
+    "df_low_high_sampled  = sample_by_regime(tidy_low_high, 10)\n",
+    "df_high_low_sampled   = sample_by_regime(tidy_high_low, 10)\n",
+    "df_low_low_sampled   = sample_by_regime(tidy_low_low, 10)\n",
+    "\n",
+    "tidy_subset = pd.concat(\n",
+    "    [df_high_high_sampled, df_low_high_sampled, df_high_low_sampled, df_low_low_sampled],\n",
+    "    ignore_index=True\n",
+    ")\n",
+    "\n",
+    "print(tidy_subset[\"regime\"].value_counts())\n",
+    "print(tidy_subset[\"regime\"].value_counts(normalize=True))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "5eee6c52",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "regime\n",
+      "Low-Low      3649320\n",
+      "Low-High      709920\n",
+      "High-High     140760\n",
+      "Name: count, dtype: int64\n",
+      "regime\n",
+      "Low-Low      0.81096\n",
+      "Low-High     0.15776\n",
+      "High-High    0.03128\n",
+      "Name: proportion, dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(tidy_df[\"regime\"].value_counts())\n",
+    "print(tidy_df[\"regime\"].value_counts(normalize=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "b64c6a5b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sort properly\n",
+    "tidy_subset = tidy_subset.sort_values([\"id\", \"date\"])\n",
+    "\n",
+    "# Per-SKU day index (1..T within each id)\n",
+    "tidy_subset[\"day_idx\"] = (\n",
+    "    tidy_subset\n",
+    "    .groupby(\"id\")[\"date\"]\n",
+    "    .rank(method=\"first\")\n",
+    "    .astype(int)\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "5c1e9ab1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sort properly\n",
+    "tidy_subset = tidy_subset.sort_values([\"id\", \"date\"])\n",
+    "\n",
+    "# Per-SKU day index (1..T within each id)\n",
+    "tidy_subset[\"day_idx\"] = (\n",
+    "    tidy_subset\n",
+    "    .groupby(\"id\")[\"date\"]\n",
+    "    .rank(method=\"first\")\n",
+    "    .astype(int)\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "fd106fa4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a=b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "57941d12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For 90-day series: use first 62 days as train, last 28 as test\n",
+    "TRAIN_HORIZON_END = 62   # 90 - 28\n",
+    "HORIZON = 14\n",
+    "\n",
+    "train_df = tidy_subset[tidy_subset[\"day_idx\"] <= TRAIN_HORIZON_END]\n",
+    "test_df  = tidy_subset[tidy_subset[\"day_idx\"] > TRAIN_HORIZON_END]\n",
+    "\n",
+    "# Inference input for LGBM (last 200 days equivalent; here min(200, series_len))\n",
+    "# For 90-day series you might just use last 62 or so; here we take last 62:\n",
+    "inference_input_df_lgbm = tidy_subset[\n",
+    "    tidy_subset[\"day_idx\"] > (TRAIN_HORIZON_END - 62)\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "2b4ae0dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>date</th>\n",
+       "      <th>city_id</th>\n",
+       "      <th>store_id</th>\n",
+       "      <th>product_id</th>\n",
+       "      <th>management_group_id</th>\n",
+       "      <th>category_1</th>\n",
+       "      <th>category_2</th>\n",
+       "      <th>category_3</th>\n",
+       "      <th>sales</th>\n",
+       "      <th>...</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>T</th>\n",
+       "      <th>N</th>\n",
+       "      <th>ADI</th>\n",
+       "      <th>CV2</th>\n",
+       "      <th>ADI_class</th>\n",
+       "      <th>CV2_class</th>\n",
+       "      <th>regime</th>\n",
+       "      <th>day_idx</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181</td>\n",
+       "      <td>2024-03-28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>129</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>33</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.805556</td>\n",
+       "      <td>0.894975</td>\n",
+       "      <td>90</td>\n",
+       "      <td>66</td>\n",
+       "      <td>1.363636</td>\n",
+       "      <td>1.234328</td>\n",
+       "      <td>High</td>\n",
+       "      <td>High</td>\n",
+       "      <td>High-High</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181</td>\n",
+       "      <td>2024-03-29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>129</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>33</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.805556</td>\n",
+       "      <td>0.894975</td>\n",
+       "      <td>90</td>\n",
+       "      <td>66</td>\n",
+       "      <td>1.363636</td>\n",
+       "      <td>1.234328</td>\n",
+       "      <td>High</td>\n",
+       "      <td>High</td>\n",
+       "      <td>High-High</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181</td>\n",
+       "      <td>2024-03-30</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>129</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>33</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.805556</td>\n",
+       "      <td>0.894975</td>\n",
+       "      <td>90</td>\n",
+       "      <td>66</td>\n",
+       "      <td>1.363636</td>\n",
+       "      <td>1.234328</td>\n",
+       "      <td>High</td>\n",
+       "      <td>High</td>\n",
+       "      <td>High-High</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181</td>\n",
+       "      <td>2024-03-31</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>129</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>33</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.805556</td>\n",
+       "      <td>0.894975</td>\n",
+       "      <td>90</td>\n",
+       "      <td>66</td>\n",
+       "      <td>1.363636</td>\n",
+       "      <td>1.234328</td>\n",
+       "      <td>High</td>\n",
+       "      <td>High</td>\n",
+       "      <td>High-High</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181</td>\n",
+       "      <td>2024-04-01</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>129</td>\n",
+       "      <td>6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>33</td>\n",
+       "      <td>181</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.805556</td>\n",
+       "      <td>0.894975</td>\n",
+       "      <td>90</td>\n",
+       "      <td>66</td>\n",
+       "      <td>1.363636</td>\n",
+       "      <td>1.234328</td>\n",
+       "      <td>High</td>\n",
+       "      <td>High</td>\n",
+       "      <td>High-High</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 33 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             id       date city_id store_id  \\\n",
+       "0  CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181 2024-03-28       0        0   \n",
+       "1  CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181 2024-03-29       0        0   \n",
+       "2  CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181 2024-03-30       0        0   \n",
+       "3  CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181 2024-03-31       0        0   \n",
+       "4  CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181 2024-04-01       0        0   \n",
+       "\n",
+       "  product_id management_group_id category_1 category_2 category_3  sales  ...  \\\n",
+       "0        129                   6         10         33        181    0.0  ...   \n",
+       "1        129                   6         10         33        181    0.0  ...   \n",
+       "2        129                   6         10         33        181    0.0  ...   \n",
+       "3        129                   6         10         33        181    0.0  ...   \n",
+       "4        129                   6         10         33        181    0.8  ...   \n",
+       "\n",
+       "       mean       std   T   N       ADI       CV2  ADI_class CV2_class  \\\n",
+       "0  0.805556  0.894975  90  66  1.363636  1.234328       High      High   \n",
+       "1  0.805556  0.894975  90  66  1.363636  1.234328       High      High   \n",
+       "2  0.805556  0.894975  90  66  1.363636  1.234328       High      High   \n",
+       "3  0.805556  0.894975  90  66  1.363636  1.234328       High      High   \n",
+       "4  0.805556  0.894975  90  66  1.363636  1.234328       High      High   \n",
+       "\n",
+       "      regime  day_idx  \n",
+       "0  High-High        1  \n",
+       "1  High-High        2  \n",
+       "2  High-High        3  \n",
+       "3  High-High        4  \n",
+       "4  High-High        5  \n",
+       "\n",
+       "[5 rows x 33 columns]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "a213e293",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.makedirs(\"processed\", exist_ok=True)\n",
+    "\n",
+    "tidy_subset.to_csv(\"processed/freshretailnet_subset.csv\", index=False)\n",
+    "train_df.to_csv(\"processed/train.csv\", index=False)\n",
+    "test_df.to_csv(\"processed/test.csv\", index=False)\n",
+    "inference_input_df_lgbm.to_csv(\n",
+    "    \"processed/inference_input_df_lgbm.csv\",\n",
+    "    index=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf3c7bf8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "sandbox",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

data/prepare_data_freshnet.py

@@ -0,0 +1,189 @@
+# %%
+import pandas as pd
+import numpy as np
+
+np.random.seed(42)
+
+# 1. Load HF dataset and convert to pandas
+raw_df = pd.read_parquet("data/raw/FreshRetailNet-50K/train.parquet")
+df_fresh_eval = pd.read_parquet("data/raw/FreshRetailNet-50K/eval.parquet")
+
+
+# %%
+# raw_df = raw_df.head(2000)
+# 2. Rename for cleanliness
+df = raw_df.rename(columns={
+    "dt": "date",
+    "sale_amount": "sales",
+    "first_category_id": "category_1",
+    "second_category_id": "category_2",
+    "third_category_id": "category_3",
+    "holiday_flag": "holiday",
+    "precpt": "precip",
+    "avg_temperature": "temp",
+    "avg_humidity": "humidity",
+    "avg_wind_level": "wind_level",
+})
+
+df["date"] = pd.to_datetime(df["date"])
+
+# 3. Build a stable SKU-ID like you did for M5
+#    here: city–store–product triple → id
+df["id"] = "CID" + df["city_id"].astype(str) + "_SID" + df["store_id"].astype(str) + "_PID" + df["product_id"].astype(str) + "_MGID" + \
+    df["management_group_id"].astype(str) + "_CAT1" + df["category_1"].astype(str) + "-CAT2" + df["category_2"].astype(str) + "-CAT3" + df["category_3"].astype(str)
+
+# %%
+def extract_daily_features(row):
+    hs = np.array(row["hours_sale"], dtype=float)        # length 24
+    st = np.array(row["hours_stock_status"], dtype=int)  # 1 = out-of-stock
+
+    sale_hours = (hs > 0).sum()
+    sale_hour_ratio = sale_hours / 24.0
+
+    stockout_hours = st.sum()
+    stockout_hour_ratio = stockout_hours / 24.0
+    avail_hour_ratio = 1.0 - stockout_hour_ratio
+    
+    return pd.Series({
+        "sale_hours": sale_hours,
+        "sale_hour_ratio": sale_hour_ratio,
+        "stockout_hours": stockout_hours,
+        "stockout_hour_ratio": stockout_hour_ratio,
+        "avail_hour_ratio": avail_hour_ratio,
+    })
+
+daily_feats = df.apply(extract_daily_features, axis=1)
+df = pd.concat([df, daily_feats], axis=1)
+
+
+# %%
+tidy_df = df[['id',
+    "date",
+    "city_id", "store_id", "product_id",
+    "management_group_id", "category_1", "category_2", "category_3",
+    "sales",
+    "sale_hours", "sale_hour_ratio",
+    "stockout_hours", "stockout_hour_ratio", "avail_hour_ratio",
+    "stock_hour6_22_cnt",
+    "discount", "holiday", "activity_flag",
+    "precip", "temp", "humidity", "wind_level",
+]].copy()
+
+# %%
+g = tidy_df.groupby("id")["sales"]
+
+summary = g.agg(["mean", "std", "count"])
+summary = summary.rename(columns={"count": "T"})
+
+summary["N"] = g.apply(lambda x: (x > 0).sum())
+summary["ADI"] = summary["T"] / summary["N"].replace(0, 1)
+summary["CV2"] = (summary["std"] / summary["mean"].replace(0, 1)) ** 2
+
+summary["ADI_class"] = np.where(summary["ADI"] > 1.32, "High", "Low")
+summary["CV2_class"] = np.where(summary["CV2"] > 0.49, "High", "Low")
+summary["regime"] = summary["ADI_class"] + "-" + summary["CV2_class"]
+
+
+# %%
+tidy_df = tidy_df.merge(summary, on="id", how="left")
+
+# %%
+tidy_high_high = tidy_df[tidy_df["regime"] == "High-High"]
+tidy_low_high  = tidy_df[tidy_df["regime"] == "Low-High"]
+tidy_high_low   = tidy_df[tidy_df["regime"] == "High-Low"]
+tidy_low_low   = tidy_df[tidy_df["regime"] == "Low-Low"]
+
+def sample_by_regime(df_regime: pd.DataFrame, num_ids_needed: int) -> pd.DataFrame:
+    """
+    Sample num_ids_needed unique IDs from df_regime and return all their history.
+    """
+    concat_df = []
+    for i, sku_id in enumerate(df_regime["id"].unique()):
+        if i < num_ids_needed:
+            concat_df.append(df_regime[df_regime["id"] == sku_id])
+        else:
+            break
+    if not concat_df:
+        return pd.DataFrame(columns=df_regime.columns)
+    return pd.concat(concat_df, ignore_index=True)
+
+multiples = 3
+df_high_high_sampled = sample_by_regime(tidy_high_high, 3 * multiples)
+df_low_high_sampled  = sample_by_regime(tidy_low_high, 15 * multiples)
+df_high_low_sampled   = sample_by_regime(tidy_high_low, 10 * multiples)
+df_low_low_sampled   = sample_by_regime(tidy_low_low, 81 * multiples)
+tidy_subset = pd.concat(
+    [df_high_high_sampled, df_low_high_sampled, df_high_low_sampled, df_low_low_sampled],
+    ignore_index=True
+)
+
+print(tidy_subset["regime"].value_counts())
+print(tidy_subset["regime"].value_counts(normalize=True))
+
+
+# %%
+# Sort properly
+tidy_subset = tidy_subset.sort_values(["id", "date"])
+
+# Per-SKU day index (1..T within each id)
+tidy_subset["day_idx"] = (
+    tidy_subset
+    .groupby("id")["date"]
+    .rank(method="first")
+    .astype(int)
+)
+
+
+# %%
+# Sort properly
+tidy_subset = tidy_subset.sort_values(["id", "date"])
+
+# Per-SKU day index (1..T within each id)
+tidy_subset["day_idx"] = (
+    tidy_subset
+    .groupby("id")["date"]
+    .rank(method="first")
+    .astype(int)
+)
+
+
+# %%
+# a=b
+
+# %%
+# For 90-day series: use first 76 days as train, last 14 as test
+HORIZON = 14
+TRAIN_HORIZON_END = 90-HORIZON   # 90 - 28
+
+
+train_df = tidy_subset[tidy_subset["day_idx"] <= TRAIN_HORIZON_END]
+test_df  = tidy_subset[tidy_subset["day_idx"] > TRAIN_HORIZON_END]
+
+# Inference input for LGBM (last 200 days equivalent; here min(200, series_len))
+# For 90-day series you might just use last 62 or so; here we take last 62:
+inference_input_df_lgbm = tidy_subset[
+    tidy_subset["day_idx"] > (TRAIN_HORIZON_END - 62)
+]
+
+# %%
+train_df.head()
+
+train_df["sales"] = train_df["sales"] * 100
+test_df["sales"] = test_df["sales"] * 100
+inference_input_df_lgbm["sales"] = inference_input_df_lgbm["sales"] * 100
+# %%
+import os
+os.makedirs("data/processed", exist_ok=True)
+
+tidy_subset.to_csv("data/processed/freshretailnet_subset.csv", index=False)
+train_df.to_csv("data/processed/train.csv", index=False)
+test_df.to_csv("data/processed/test.csv", index=False)
+inference_input_df_lgbm.to_csv(
+    "data/processed/inference_input_df_lgbm.csv",
+    index=False
+)
+
+# %%
+
+
+

data/prepare_data_lgbm_fresh.py

@@ -0,0 +1,199 @@
+import pandas as pd
+import numpy as np
+import os
+
+# -------------------------------------------------------------------
+# Load train/test built from FreshRetailNet-50K
+# (from your new prepare_freshretailnet_subset.py pipeline)
+# -------------------------------------------------------------------
+train_df = pd.read_csv("data/processed/train.csv")
+test_df = pd.read_csv("data/processed/test.csv")
+
+print("Train columns:", train_df.columns.tolist())
+
+# -------------------------------------------------------------------
+# Helpers
+# -------------------------------------------------------------------
+def rename_columns(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Rename pivoted time-step columns so they start from 1 and increment
+    by 1, regardless of original day_idx values.
+
+    Input columns: ["id", t1, t2, ..., tN]  (t's are ints)
+    Output cols:   ["id", 1, 2, ..., N]
+    """
+    other_cols = [c for c in df.columns if c != "id"]
+    if not other_cols:
+        return df
+
+    min_val = min(other_cols)
+    new_cols = [int(c - min_val + 1) for c in other_cols]
+
+    df.columns = ["id"] + new_cols
+    return df
+
+
+# -------------------------------------------------------------------
+# CONFIG — automatically adapt to train length per id
+# -------------------------------------------------------------------
+# we keep your original 14-step validation for LGBM
+lgbm_val_length = 14
+
+# infer per-id train series length from the first id
+_first_id = train_df["id"].iloc[0]
+_series_len_train = train_df[train_df["id"] == _first_id].shape[0]
+
+# ensure it's consistent across all ids
+assert (
+    train_df.groupby("id").size().nunique() == 1
+), "All ids in train_df must have same length."
+
+lgbm_train_length = _series_len_train - lgbm_val_length
+print(f"Detected train length per id: {_series_len_train}")
+print(f"LGBM train length: {lgbm_train_length}, val length: {lgbm_val_length}")
+
+# -------------------------------------------------------------------
+# 1) BUILD TRAIN/TARGET FOR MULTI-OUTPUT LGBM
+# -------------------------------------------------------------------
+train_dfs = []
+target_dfs = []
+
+for unique_id in train_df["id"].unique():
+    subset_df = train_df[train_df["id"] == unique_id].copy()
+
+    # Drop non-time-series numeric features; keep only id, day_idx, sales
+    drop_cols = [
+        "product_id",
+        "category_1",
+        "category_2",
+        "category_3",
+        "store_id",
+        "city_id",
+        "management_group_id",
+        "sale_hours",
+        "sale_hour_ratio",
+        "stock_hour6_22_cnt",
+        "stockout_hours",
+        "stockout_hour_ratio",
+        "avail_hour_ratio",
+        "discount",
+        "holiday",
+        "activity_flag",
+        "precip",
+        "temp",
+        "humidity",
+        "wind_level",
+        "mean",
+        "std",
+        "T",
+        "N",
+        "ADI",
+        "CV2",
+        "ADI_class",
+        "CV2_class",
+        "regime",
+    ]
+    drop_cols = [c for c in drop_cols if c in subset_df.columns]
+    subset_df = subset_df.drop(columns=drop_cols)
+
+    # We rely on day_idx as the time axis (1..62)
+    # sort just to be sure
+    subset_df = subset_df.sort_values("day_idx")
+
+    # Rolling windows in the training set
+    for start in range(0, len(subset_df), lgbm_train_length + lgbm_val_length):
+        train_slice = subset_df.iloc[start : start + lgbm_train_length]
+        test_slice = subset_df.iloc[
+            start + lgbm_train_length : start + lgbm_train_length + lgbm_val_length
+        ]
+
+        # skip incomplete windows
+        if len(train_slice) < lgbm_train_length or len(test_slice) < lgbm_val_length:
+            continue
+
+        # Wide format for multi-output regression: id × time_steps
+        train_wide = (
+            train_slice.pivot(index="id", columns="day_idx", values="sales")
+            .reset_index()
+        )
+        test_wide = (
+            test_slice.pivot(index="id", columns="day_idx", values="sales")
+            .reset_index()
+        )
+
+        train_wide = rename_columns(train_wide)
+        test_wide = rename_columns(test_wide)
+
+        train_dfs.append(train_wide)
+        target_dfs.append(test_wide)
+
+# Concatenate all windows
+if not train_dfs or not target_dfs:
+    raise RuntimeError("No valid LGBM windows were created from train_df.")
+
+train_lgbm = pd.concat(train_dfs, ignore_index=True)
+target_lgbm = pd.concat(target_dfs, ignore_index=True)
+
+# Make directory if not exists
+os.makedirs("data/processed/lgbm_ready", exist_ok=True)
+
+# Save
+train_lgbm.to_csv("data/processed/lgbm_ready/train.csv", index=False)
+target_lgbm.to_csv("data/processed/lgbm_ready/target.csv", index=False)
+
+print("Saved LGBM train/target to data/processed/lgbm_ready/")
+
+# -------------------------------------------------------------------
+# 2) BUILD INFERENCE TRAIN/TARGET (LAST WINDOW)
+# -------------------------------------------------------------------
+train_df = pd.read_csv("data/processed/train.csv")
+test_df = pd.read_csv("data/processed/test.csv")
+
+inference_dfs = []
+validation_dfs = []
+
+for unique_id in train_df["id"].unique():
+    subset_train = train_df[train_df["id"] == unique_id].copy()
+    subset_train = subset_train.sort_values("day_idx")
+
+    # last lgbm_train_length points of the train series
+    start_idx = subset_train.shape[0] - lgbm_train_length
+    inference_slice = subset_train.iloc[start_idx : start_idx + lgbm_train_length]
+
+    inference_wide = (
+        inference_slice.pivot(index="id", columns="day_idx", values="sales")
+        .reset_index()
+    )
+    inference_wide = rename_columns(inference_wide)
+
+    # corresponding last lgbm_val_length points from test series
+    subset_test = test_df[test_df["id"] == unique_id].copy()
+    subset_test = subset_test.sort_values("day_idx")
+
+    start_val = subset_test.shape[0] - lgbm_val_length
+    validation_slice = subset_test.iloc[start_val : start_val + lgbm_val_length]
+
+    validation_wide = (
+        validation_slice.pivot(index="id", columns="day_idx", values="sales")
+        .reset_index()
+    )
+    validation_wide = rename_columns(validation_wide)
+
+    inference_dfs.append(inference_wide)
+    validation_dfs.append(validation_wide)
+
+inference_df = pd.concat(inference_dfs, ignore_index=True)
+validation_df = pd.concat(validation_dfs, ignore_index=True)
+
+os.makedirs("data/processed/lgbm_ready/inference", exist_ok=True)
+
+inference_df.to_csv(
+    "data/processed/lgbm_ready/inference/inference_train.csv",
+    index=False,
+)
+validation_df.to_csv(
+    "data/processed/lgbm_ready/inference/inference_target.csv",
+    index=False,
+)
+
+print("Saved LGBM inference train/target to data/processed/lgbm_ready/inference/")

data/processed/eval1.csv

@@ -0,0 +1,3 @@
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+oid sha256:85b85d8483cf78fecdda836eb01fd75ee758ecbe9e9a008e9afc3fed01311062
+size 47404608

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+CID0_SID0_PID796_MGID6_CAT14-CAT228-CAT31,170.0,70.0,140.0,180.0,240.0,150.0,80.0,230.0,90.0,80.0,250.0,220.00000000000003,100.0,160.0
+CID0_SID0_PID806_MGID6_CAT121-CAT264-CAT3184,10.0,50.0,30.0,80.0,80.0,20.0,70.0,60.0,80.0,40.0,70.0,50.0,40.0,30.0
+CID0_SID0_PID810_MGID6_CAT14-CAT228-CAT3168,70.0,20.0,70.0,150.0,140.0,80.0,160.0,110.0,150.0,150.0,200.0,0.0,180.0,220.00000000000003
+CID0_SID0_PID834_MGID0_CAT128-CAT272-CAT3154,210.0,130.0,260.0,170.0,140.0,270.0,190.0,290.0,280.0,280.0,420.0,310.0,240.0,240.0
+CID0_SID0_PID843_MGID6_CAT14-CAT228-CAT3167,60.0,40.0,50.0,40.0,60.0,10.0,90.0,80.0,20.0,90.0,60.0,80.0,20.0,70.0
+CID0_SID0_PID90_MGID6_CAT14-CAT253-CAT377,270.0,170.0,330.0,120.0,40.0,0.0,0.0,150.0,190.0,70.0,280.0,30.0,140.0,220.00000000000003
+CID0_SID0_PID93_MGID6_CAT121-CAT261-CAT3223,50.0,50.0,80.0,120.0,60.0,40.0,50.0,100.0,30.0,140.0,70.0,30.0,60.0,100.0
+CID0_SID12_PID129_MGID6_CAT110-CAT233-CAT3181,40.0,50.0,10.0,70.0,120.0,20.0,60.0,60.0,20.0,10.0,50.0,20.0,30.0,40.0
+CID0_SID12_PID41_MGID6_CAT14-CAT253-CAT377,70.0,50.0,40.0,50.0,70.0,50.0,70.0,130.0,70.0,90.0,70.0,130.0,60.0,30.0
+CID0_SID12_PID768_MGID0_CAT15-CAT25-CAT36,90.0,0.0,60.0,260.0,10.0,10.0,100.0,390.0,10.0,0.0,0.0,0.0,0.0,0.0
+CID0_SID18_PID362_MGID1_CAT17-CAT217-CAT351,60.0,110.0,120.0,150.0,60.0,110.0,110.0,120.0,160.0,180.0,130.0,140.0,230.0,160.0
+CID0_SID18_PID536_MGID0_CAT15-CAT26-CAT365,0.0,20.0,0.0,120.0,0.0,0.0,0.0,140.0,130.0,220.00000000000003,190.0,230.0,290.0,230.0
+CID0_SID18_PID830_MGID1_CAT17-CAT216-CAT344,20.0,30.0,80.0,80.0,0.0,60.0,40.0,20.0,30.0,20.0,40.0,10.0,10.0,0.0
+CID0_SID19_PID768_MGID0_CAT15-CAT25-CAT36,120.0,60.0,30.0,0.0,100.0,0.0,30.0,60.0,30.0,90.0,90.0,60.0,100.0,60.0
+CID0_SID1_PID104_MGID6_CAT120-CAT250-CAT32,370.0,430.0,550.0,650.0,720.0,360.0,630.0,400.0,460.0,650.0,740.0,560.0,670.0,570.0
+CID0_SID1_PID108_MGID6_CAT18-CAT229-CAT3113,0.0,0.0,0.0,20.0,130.0,80.0,110.0,50.0,60.0,70.0,100.0,30.0,90.0,50.0
+CID0_SID1_PID110_MGID2_CAT131-CAT279-CAT3121,50.0,40.0,60.0,80.0,70.0,40.0,30.0,70.0,10.0,40.0,70.0,80.0,20.0,60.0
+CID0_SID1_PID114_MGID6_CAT110-CAT233-CAT372,220.00000000000003,80.0,20.0,360.0,280.0,220.00000000000003,160.0,300.0,280.0,270.0,340.0,470.0,230.0,430.0
+CID0_SID1_PID117_MGID6_CAT14-CAT228-CAT31,290.0,430.0,30.0,720.0,910.0,640.0,780.0,760.0,660.0,780.0,760.0,960.0,580.0,830.0000000000001
+CID0_SID1_PID118_MGID6_CAT14-CAT228-CAT3180,110.0,190.0,180.0,260.0,220.00000000000003,160.0,160.0,250.0,200.0,160.0,360.0,210.0,80.0,160.0
+CID0_SID1_PID121_MGID2_CAT130-CAT274-CAT3195,20.0,30.0,40.0,200.0,60.0,40.0,80.0,30.0,50.0,30.0,110.0,50.0,50.0,50.0
+CID0_SID1_PID122_MGID6_CAT120-CAT268-CAT3127,210.0,260.0,330.0,520.0,400.0,300.0,370.0,300.0,420.0,470.0,410.0,700.0,460.0,760.0
+CID0_SID1_PID127_MGID6_CAT120-CAT258-CAT3172,70.0,80.0,200.0,180.0,190.0,80.0,100.0,150.0,100.0,150.0,130.0,260.0,70.0,100.0
+CID0_SID1_PID133_MGID2_CAT131-CAT277-CAT373,60.0,20.0,70.0,130.0,110.0,60.0,70.0,50.0,50.0,50.0,110.0,140.0,30.0,120.0
+CID0_SID1_PID136_MGID6_CAT14-CAT228-CAT3161,70.0,70.0,60.0,80.0,120.0,100.0,50.0,50.0,110.0,100.0,60.0,130.0,40.0,50.0
+CID0_SID1_PID138_MGID4_CAT127-CAT237-CAT3126,40.0,50.0,70.0,60.0,40.0,30.0,60.0,50.0,40.0,90.0,100.0,70.0,60.0,110.0
+CID0_SID1_PID140_MGID6_CAT14-CAT228-CAT310,100.0,90.0,120.0,90.0,50.0,130.0,50.0,100.0,100.0,80.0,80.0,110.0,80.0,90.0
+CID0_SID1_PID151_MGID2_CAT131-CAT279-CAT3158,40.0,30.0,20.0,60.0,50.0,30.0,30.0,40.0,30.0,30.0,60.0,60.0,30.0,40.0
+CID0_SID1_PID166_MGID6_CAT120-CAT250-CAT359,410.0,500.0,248.0,520.0,610.0,320.0,330.0,280.0,270.0,330.0,330.0,400.0,220.00000000000003,400.0
+CID0_SID1_PID16_MGID6_CAT18-CAT229-CAT3113,0.0,0.0,40.0,40.0,90.0,30.0,70.0,130.0,70.0,20.0,80.0,30.0,10.0,10.0
+CID0_SID1_PID17_MGID6_CAT120-CAT258-CAT3172,20.0,30.0,30.0,40.0,60.0,20.0,60.0,60.0,70.0,60.0,40.0,60.0,30.0,50.0
+CID0_SID1_PID18_MGID6_CAT14-CAT228-CAT3142,90.0,20.0,120.0,100.0,100.0,130.0,110.0,150.0,90.0,30.0,170.0,130.0,80.0,90.0
+CID0_SID1_PID190_MGID6_CAT14-CAT228-CAT3131,220.00000000000003,170.0,220.00000000000003,220.00000000000003,150.0,210.0,160.0,210.0,220.00000000000003,240.0,0.0,0.0,0.0,220.00000000000003
+CID0_SID1_PID194_MGID5_CAT118-CAT280-CAT3109,60.0,133.0,90.0,210.0,460.0,90.0,140.0,290.0,120.0,140.0,190.0,210.0,110.0,180.0
+CID0_SID1_PID19_MGID6_CAT14-CAT228-CAT381,310.0,210.0,250.0,160.0,180.0,180.0,260.0,230.0,400.0,210.0,330.0,250.0,10.0,270.0
+CID0_SID1_PID200_MGID6_CAT18-CAT229-CAT3113,0.0,50.0,90.0,40.0,50.0,20.0,90.0,40.0,30.0,60.0,48.0,70.0,30.0,40.0
+CID0_SID1_PID201_MGID6_CAT18-CAT229-CAT3113,130.0,140.0,120.0,160.0,190.0,160.0,250.0,120.0,130.0,110.0,150.0,140.0,160.0,210.0
+CID0_SID1_PID207_MGID6_CAT14-CAT228-CAT3179,160.0,100.0,200.0,160.0,170.0,120.0,270.0,280.0,200.0,260.0,270.0,250.0,140.0,210.0
+CID0_SID1_PID213_MGID6_CAT14-CAT228-CAT381,10.0,40.0,40.0,30.0,60.0,50.0,50.0,60.0,50.0,70.0,50.0,70.0,70.0,100.0
+CID0_SID1_PID214_MGID6_CAT18-CAT28-CAT39,30.0,70.0,50.0,100.0,40.0,40.0,80.0,60.0,60.0,90.0,70.0,120.0,80.0,90.0
+CID0_SID1_PID215_MGID6_CAT14-CAT228-CAT3149,600.0,500.0,710.0,620.0,540.0,670.0,630.0,730.0,670.0,760.0,850.0,600.0,400.0,1230.0
+CID0_SID1_PID216_MGID6_CAT121-CAT264-CAT3123,50.0,60.0,70.0,110.0,60.0,50.0,60.0,80.0,70.0,140.0,90.0,100.0,40.0,50.0
+CID0_SID1_PID219_MGID6_CAT18-CAT229-CAT3114,100.0,60.0,50.0,80.0,20.0,40.0,20.0,80.0,40.0,80.0,100.0,20.0,30.0,50.0
+CID0_SID1_PID220_MGID6_CAT18-CAT229-CAT3113,40.0,30.0,30.0,90.0,80.0,20.0,50.0,60.0,10.0,0.0,130.0,0.0,50.0,70.0
+CID0_SID1_PID23_MGID6_CAT14-CAT253-CAT358,30.0,30.0,30.0,70.0,170.0,130.0,180.0,200.0,260.0,220.00000000000003,120.0,260.0,120.0,94.0
+CID0_SID1_PID247_MGID5_CAT116-CAT225-CAT394,120.0,60.0,40.0,120.0,30.0,90.0,60.0,100.0,60.0,100.0,90.0,120.0,80.0,50.0
+CID0_SID1_PID249_MGID5_CAT116-CAT225-CAT3103,17.0,30.0,30.0,20.0,30.0,50.0,30.0,40.0,50.0,0.0,20.0,53.0,10.0,60.0
+CID0_SID1_PID250_MGID5_CAT116-CAT227-CAT397,60.0,60.0,80.0,50.0,80.0,70.0,40.0,90.0,120.0,90.0,70.0,100.0,60.0,120.0
+CID0_SID1_PID258_MGID2_CAT131-CAT279-CAT3121,70.0,50.0,60.0,90.0,100.0,40.0,40.0,40.0,80.0,20.0,100.0,50.0,60.0,30.0
+CID0_SID1_PID259_MGID6_CAT121-CAT264-CAT3123,90.0,100.0,30.0,110.0,140.0,90.0,0.0,90.0,20.0,70.0,0.0,0.0,30.0,80.0
+CID0_SID1_PID26_MGID6_CAT14-CAT253-CAT3156,80.0,70.0,120.0,120.0,10.0,140.0,70.0,120.0,80.0,77.0,110.0,200.0,70.0,150.0
+CID0_SID1_PID27_MGID6_CAT14-CAT228-CAT310,30.0,20.0,10.0,30.0,10.0,0.0,30.0,10.0,20.0,60.0,30.0,30.0,10.0,30.0
+CID0_SID1_PID285_MGID6_CAT124-CAT251-CAT3203,30.0,0.0,20.0,80.0,70.0,10.0,60.0,50.0,30.0,30.0,90.0,100.0,10.0,20.0
+CID0_SID1_PID290_MGID5_CAT116-CAT225-CAT3105,180.0,160.0,170.0,110.0,100.0,200.0,130.0,210.0,200.0,290.0,260.0,370.0,160.0,180.0
+CID0_SID1_PID291_MGID5_CAT116-CAT227-CAT398,40.0,57.00000000000001,70.0,70.0,60.0,70.0,70.0,100.0,110.0,150.0,70.0,130.0,90.0,150.0
+CID0_SID1_PID292_MGID5_CAT116-CAT227-CAT3104,230.0,240.0,240.0,240.0,130.0,200.0,180.0,220.00000000000003,250.0,0.0,280.0,320.0,150.0,280.0
+CID0_SID1_PID293_MGID5_CAT116-CAT227-CAT3100,44.00000000000001,20.0,70.0,60.0,40.0,50.0,50.0,30.0,60.0,60.0,70.0,90.0,40.0,110.0
+CID0_SID1_PID295_MGID5_CAT116-CAT225-CAT3106,50.0,30.0,40.0,40.0,20.0,60.0,20.0,50.0,80.0,80.0,70.0,90.0,20.0,80.0
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+CID0_SID1_PID300_MGID6_CAT120-CAT250-CAT324,540.0,190.0,720.0,1140.0,1180.0,1000.0,220.00000000000003,950.0,1170.0,1250.0,1420.0,1440.0,1040.0,1340.0
+CID0_SID1_PID304_MGID2_CAT131-CAT279-CAT3227,20.0,30.0,20.0,50.0,40.0,40.0,30.0,40.0,40.0,50.0,60.0,70.0,60.0,60.0
+CID0_SID1_PID321_MGID5_CAT118-CAT280-CAT3109,30.0,80.0,50.0,90.0,90.0,50.0,50.0,50.0,60.0,60.0,110.0,100.0,50.0,90.0
+CID0_SID1_PID345_MGID5_CAT116-CAT226-CAT396,80.0,70.0,100.0,90.0,70.0,80.0,80.0,100.0,60.0,110.0,110.0,110.0,60.0,110.0
+CID0_SID1_PID362_MGID1_CAT17-CAT217-CAT351,50.0,60.0,80.0,100.0,60.0,90.0,100.0,100.0,120.0,160.0,200.0,150.0,130.0,200.0
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+CID0_SID1_PID368_MGID5_CAT118-CAT280-CAT3112,30.0,20.0,20.0,40.0,50.0,60.0,10.0,60.0,10.0,40.0,60.0,60.0,40.0,40.0
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+CID0_SID1_PID379_MGID2_CAT129-CAT276-CAT3231,90.0,80.0,80.0,120.0,140.0,70.0,80.0,70.0,70.0,100.0,110.0,120.0,60.0,70.0
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+CID0_SID1_PID4_MGID2_CAT129-CAT278-CAT382,100.0,100.0,220.00000000000003,390.0,410.0,90.0,190.0,90.0,90.0,210.0,130.0,400.0,80.0,190.0
+CID0_SID1_PID500_MGID6_CAT14-CAT228-CAT3179,70.0,80.0,130.0,130.0,60.0,100.0,70.0,50.0,110.0,110.0,70.0,200.0,80.0,80.0
+CID0_SID1_PID554_MGID6_CAT14-CAT228-CAT3168,70.0,30.0,70.0,260.0,220.00000000000003,40.0,100.0,120.0,110.0,130.0,140.0,250.0,90.0,170.0
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+CID0_SID1_PID578_MGID6_CAT14-CAT253-CAT358,110.0,110.0,140.0,150.0,130.0,90.0,130.0,150.0,220.00000000000003,170.0,190.0,200.0,120.0,130.0
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+CID0_SID1_PID596_MGID2_CAT129-CAT276-CAT360,200.0,160.0,150.0,210.0,160.0,90.0,280.0,280.0,210.0,280.0,210.0,160.0,160.0,90.0
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data/processed/lgbm_ready/target.csv

@@ -0,0 +1,298 @@
+id,1,2,3,4,5,6,7,8,9,10,11,12,13,14
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+CID0_SID1_PID190_MGID6_CAT14-CAT228-CAT3131,190.0,100.0,160.0,260.0,220.00000000000003,150.0,150.0,110.0,170.0,180.0,50.0,190.0,0.0,0.0
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+CID0_SID1_PID207_MGID6_CAT14-CAT228-CAT3179,160.0,180.0,190.0,220.00000000000003,180.0,180.0,200.0,170.0,0.0,200.0,130.0,130.0,150.0,210.0
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+CID0_SID1_PID23_MGID6_CAT14-CAT253-CAT358,70.0,80.0,100.0,50.0,60.0,50.0,60.0,60.0,50.0,40.0,80.0,30.0,30.0,70.0
+CID0_SID1_PID247_MGID5_CAT116-CAT225-CAT394,60.0,40.0,80.0,30.0,90.0,40.0,70.0,60.0,40.0,60.0,30.0,70.0,50.0,60.0
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+CID0_SID1_PID285_MGID6_CAT124-CAT251-CAT3203,40.0,30.0,30.0,70.0,70.0,30.0,40.0,40.0,30.0,30.0,70.0,30.0,60.0,50.0
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docs/Appendix.md

@@ -0,0 +1,241 @@
+
+---
+
+# **Evidence Appendix — Why Smoothing Models and Chronos2 Form the Forecast Anchor in FreshNet**
+
+---
+
+## **A. Portfolio-Level Evidence**
+
+All models were evaluated SKU-wise using the bias-aware scoring function:
+
+```
+Score = MAE + |Bias|
+```
+
+This penalizes models that appear accurate but drift directionally—
+a critical failure mode in fresh categories where bias inflates waste or drives stockouts.
+
+### **Observed portfolio stability hierarchy (↓ = more stable)**
+
+**Tier A — Stable Forecast Models**
+
+| Model Family                            | Mean Stability Score (↓ better) |
+| --------------------------------------- | ------------------------------- |
+| **DynamicOptimizedTheta**               | 66.89                           |
+| **SimpleExponentialSmoothingOptimized** | 67.31                           |
+| **Chronos2**                            | 67.65                           |
+| **Theta**                               | 67.68                           |
+| **DynamicTheta**                        | 67.69                           |
+| **CrostonOptimized / CrostonClassic**   | 67.88–68.36                     |
+
+**Tier B — Acceptable Secondary Models**
+
+| Model         | Score |
+| ------------- | ----- |
+| WindowAverage | 68.59 |
+| HoltWinters   | 71.40 |
+| Holt          | 71.84 |
+
+**Tier C — High-Noise / High-Drift Models**
+
+| Model               | Score |
+| ------------------- | ----- |
+| SeasonalNaive       | 76.74 |
+| **LightGBM**        | 83.91 |
+| HistoricAverage     | 84.07 |
+| Naive               | 88.83 |
+| RandomWalkWithDrift | 92.74 |
+
+### **Interpretation**
+
+* Tier-A models produce **directionally stable**, low-noise forecasts.
+* ML (LightGBM), without drivers such as discount, weather, or stockout hours, becomes **unstable**, overreacting to recent noise.
+* Naive and drift models exaggerate noise and create planning churn.
+
+**Conclusion:**
+FreshNet dynamics reward smoothing, not signal chasing.
+
+---
+
+## **B. SKU-Level Model Decisions**
+
+Winner share across all evaluated SKUs:
+
+| Tier       | Model Families                                                     | Share     |
+| ---------- | ------------------------------------------------------------------ | --------- |
+| **Tier A** | **Theta-family**, **SES/Holt**, **Chronos2**, **Croston variants** | **~65%+** |
+| Tier B     | WindowAverage, HistoricAverage                                     | ~20%      |
+| **Tier C** | LightGBM, Naive, Drift                                             | ~15%      |
+
+### **Interpretation**
+
+* Winners did **not** cluster around ML models.
+* The distribution is **not symmetric**—smoothing models dominate across regimes.
+* LightGBM only wins where behavior is quasi-linear *and* no external drivers are needed.
+
+This is consistent with **fresh demand physics**, not algorithmic preference.
+
+---
+
+## **C. Behavioral Regime Analysis**
+
+FreshNet SKUs were segmented into three behavioral regimes.
+Below are the stability winners.
+
+---
+
+### **1) High-High Regime**
+
+*(unstable timing + unstable magnitude)*
+
+| Winning Families                                   |
+| -------------------------------------------------- |
+| **Theta-family models**                            |
+| **SES/Holt smoothing**                             |
+| **Chronos2**                                       |
+| Croston variants (for sparse high-volatility SKUs) |
+
+**Why they win**
+
+* They smooth volatility without flattening structure.
+* They avoid overreacting after spikes.
+* Chronos2 handles mixed signal patterns without oscillation.
+
+LightGBM heavily overfit recent bursts → poor forward stability.
+
+---
+
+### **2) Low-High Regime**
+
+*(regular recurrence, unstable amplitude)*
+
+| Winning Families |
+| ---------------- |
+| **Holt-Winters** |
+| **Theta**        |
+| **Chronos2**     |
+| Croston variants |
+
+**Interpretation**
+
+* Seasonal consistency explains Holt-Winters success.
+* Amplitude spikes are handled by smoothing models, not ML.
+* Chronos2 adapts without resetting level after shocks.
+
+---
+
+### **3) Low-Low Regime**
+
+*(stable, low-variance items)*
+
+| Winning Families             |
+| ---------------------------- |
+| **SES/Holt/Theta**           |
+| Historic Average (some SKUs) |
+| Croston (intermittent)       |
+
+**Interpretation**
+
+* Model choice matters least here.
+* Smoothing models converge to the correct baseline.
+* Chronos2 is neutral—not worse, not necessary.
+
+---
+
+## **D. Example SKU-Level Decisions (Traceable)**
+
+| SKU Identifier    | Stable Winner             |
+| ----------------- | ------------------------- |
+| CID0_SID0_PID104… | **DynamicOptimizedTheta** |
+| CID0_SID0_PID118… | **Chronos2**              |
+| CID0_SID0_PID127… | **SES/Holt**              |
+| CID0_SID0_PID319… | **CrostonSBA**            |
+| CID0_SID0_PID229… | **Holt-Winters**          |
+
+Purpose:
+
+* guarantees reproducibility
+* shows evidence of regime-matched decisions
+* prevents subjective reinterpretation
+
+---
+
+# **What the Evidence Resolves**
+
+---
+
+## **Technically**
+
+The evidence demonstrates that:
+
+* Theta/SES models **minimize drift**, the critical failure mode.
+* Chronos2 handles complex structure without overreacting.
+* Croston preserves stability for zero-heavy SKUs.
+* LightGBM is unsuitable for fresh categories **without driver data**.
+
+### Stability, not complexity, is the determinant.
+
+---
+
+## **Operationally**
+
+A stable anchor model eliminates:
+
+* excessive overrides
+* store-planner misalignment
+* week-to-week forecast resets
+* spiraling exception handling
+
+And enables:
+
+* consistent ordering
+* predictable labor/waste planning
+* clean exception signals
+
+---
+
+## **Economically**
+
+Stable models reduce:
+
+* re-forecasting cycles
+* waste from positive bias
+* stockouts from negative bias
+* planning churn and meeting load
+
+These are real cost centers in fresh operations.
+
+---
+
+# **Deployment Decision**
+
+> **Theta-family smoothing + SES/Holt + Chronos2 = the canonical fresh-forecasting ensemble.**
+> **Croston methods = the anchor for intermittent SKUs.**
+> **LightGBM = only used once driver data (discounts, stockout hours, weather) is integrated.**
+
+Fallbacks are allowed **only** when:
+
+1. a SKU is structurally deterministic (e.g., controlled replenishment)
+2. the category is end-of-life
+3. required signals are missing
+4. governance mandates a deterministic forecast
+
+All fallback choices must be recorded in the model selection ledger.
+
+---
+
+# **Closing Position**
+
+This evidence does not indicate small differences—it shows a **structural hierarchy**.
+
+**Theta/SES/Croston/Chronos2 are not slightly better—
+they are the only models that remain operationally stable across FreshNet’s volatile, mixed-pattern, and intermittent regimes.**
+
+They produce a forecast that is not only “accurate,”
+but **steady enough to drive durable planning decisions**.
+
+That is why they form the anchor set for FreshNet forecasting.
+
+---
+

docs/Executive_brief.md

@@ -0,0 +1,156 @@
+Below is the **final Executive Brief**, now **fully integrated with your executive-grade plot**.
+The plot is inserted exactly where an executive expects it, with a clean, high-signal caption and narrative bridge.
+
+You can paste this directly into a PDF or slide.
+
+---
+
+# **Executive Brief — Stabilizing Forecast Signals in Fresh Retail Environments**
+
+---
+
+## **Why This Matters in Fresh Retail**
+
+Fresh categories behave differently from ambient grocery.
+The FreshNet dataset reflects what operators experience daily:
+
+* high volatility in unit demand
+* frequent zero-sales days
+* sharp lifts driven by promotions or weather
+* stockouts that distort the forecast signal
+
+When the forecast shifts abruptly week to week, teams must repeatedly reset:
+
+* replenishment quantities
+* production planning
+* store-level ordering
+* waste and markdown expectations
+
+This rework slows execution and lowers confidence across planning teams.
+
+This analysis was designed to answer a practical question:
+
+> **Which forecasting approaches produce a stable, low-noise forward signal suitable for fresh operations?**
+
+---
+
+## **What the Evaluation Showed (FreshNet Context)**
+
+FreshNet SKUs revealed a **clear hierarchy** in forecasting stability.
+Some models consistently dampened noise; others amplified it.
+
+---
+
+## **Portfolio-Level Forecast Stability**
+
+![Portfolio-Level Model Ranking](./model_score_ranking_exec.png)
+
+**Figure:** Forecast model stability comparison (lower score = more stable, lower operational risk).
+
+This plot shows:
+
+* **Blue models** → consistently stable signal
+* **Gray models** → acceptable for some SKUs, not ideal for volatile items
+* **Red models** → produce unstable week-to-week swings, creating rework
+
+The stability threshold highlights where noise becomes operationally disruptive.
+
+---
+
+## **Key Insights From the FreshNet Evaluation**
+
+### **1. A small group of models produced the most stable, low-bias signal**
+
+These methods consistently absorbed volatility without overreacting:
+
+* **Theta-family smoothing**
+* **SES / Holt / Holt-Winters (exponential smoothing)**
+* **Chronos2** for mixed patterns
+* **Croston variants** for intermittent and zero-heavy SKUs
+
+These models form the **recommended baseline** for fresh forecasting.
+
+---
+
+### **2. Ambient-category heuristic models do not work for fresh**
+
+Models like:
+
+* naive carry-forward
+* simple averages
+* drift-based projections
+
+created **false volatility** and **unstable directional signals**, which forced planners to repeatedly correct the forecast.
+
+These methods should be avoided for fresh categories.
+
+---
+
+### **3. Machine learning requires real drivers to perform**
+
+FreshNet data shows that ML methods such as LightGBM only outperform when provided with:
+
+* discount depth
+* stockout hours
+* weather variables
+* availability ratios
+
+Without these drivers, ML becomes unstable — validating the need for **model-to-demand matching**, not model complexity.
+
+---
+
+## **Recommended Forecast Baseline for FreshNet**
+
+> **Use smoothing-based models (Theta/SES/Holt) as the default signal for FreshNet SKUs.
+> Use Croston-type methods for intermittent items.
+> Use Chronos2 when SKUs show variability combined with trend or soft seasonality.**
+
+This approach:
+
+* minimizes week-to-week swings
+* reduces bias
+* improves interpretability
+* provides planners with a reliable directional signal
+
+Exactly what fresh operations require.
+
+---
+
+## **Operational Impact for Fresh Retail**
+
+A more stable forecast directly improves:
+
+### **Order Stability**
+
+Store and central planning remain aligned with fewer urgent corrections.
+
+### **Waste Reduction**
+
+Stable baselines prevent over-ordering during temporary spikes.
+
+### **Production & Labor Planning**
+
+Teams commit earlier and avoid recalculating workflows.
+
+### **Exception Visibility**
+
+True demand shifts stand out clearly because baseline noise is lower.
+
+---
+
+## **Leadership Takeaway**
+
+**Stable forecasts create stable fresh-retail operations.
+The recommended models deliver the most reliable, low-noise baseline for FreshNet —
+supporting stronger ordering discipline, lower waste, and more confident execution.**
+
+---
+
+If you want, I can now produce:
+
+* a **PDF-ready 1-pager**
+* a **slide version using your executive theme**
+* a **technical appendix** (metrics, regimes, diagnostics)
+* a version written specifically for **CFO**, **COO**, or **Planning Directors**
+
+Tell me which format you want next.

docs/Technical_brief.md

@@ -0,0 +1,217 @@
+
+---
+
+# **Forecast Sandbox — Technical Summary (FreshNet Edition)**
+
+---
+
+## **1. Purpose**
+
+Forecasting difficulty in fresh retail is not driven only by demand uncertainty,
+but by **instability in the forward signal**.
+
+A model that swings direction week to week creates:
+
+* recurring overrides
+* repeated replanning cycles
+* loss of alignment between planners, stores, and operations
+
+The sandbox therefore answers one technical question:
+
+> **Which forecasting approaches maintain directional stability across heterogeneous fresh-category demand behaviors?**
+
+Evaluation is performed **per SKU**, not as a portfolio average, using a scoring function that explicitly penalizes drift and directional inconsistency.
+
+---
+
+## **2. Dataset Basis — FreshNet**
+
+This evaluation uses the **FreshNet retail dataset**, which reflects the operational characteristics of fresh categories:
+
+* highly volatile daily demand
+* frequent zero-sales days
+* stockout-induced distortions
+* promotion and weather sensitivity
+* abrupt shifts rather than smooth seasonal patterns
+
+These dynamics make fresh forecasting fundamentally different from ambient categories.
+
+A behavioral segmentation was created:
+
+| Regime    | Description                             |
+| --------- | --------------------------------------- |
+| High-High | unstable frequency + unstable magnitude |
+| Low-High  | regular timing + high variability       |
+| Low-Low   | stable, low-variance items              |
+
+The purpose of segmentation is not accuracy benchmarking but **stability differentiation** under heterogeneous behaviors.
+
+---
+
+## **3. Execution Workflow**
+
+The sandbox is fully orchestrated using:
+
+```
+python run_pipeline.py
+```
+
+This executes the following sequence:
+
+```
+prepare_data
+prepare_data_lgbm
+compute_baselines
+lgbm_modeling
+chronos_inference
+combine_metrics
+select_best_model
+generate_first_insights
+model_selection_audit
+```
+
+Outputs include:
+
+* SKU-level model forecasts
+* consolidated metric ledger
+* per-regime evidence tables
+* full audit log of model selection decisions
+
+All results are **reproducible**, deterministic, and governed by transparent rules.
+
+---
+
+## **4. Scoring Function**
+
+For each SKU and model:
+
+```
+score = MAE + |Bias|
+```
+
+Rationale:
+
+* **MAE** penalizes magnitude error
+* **Bias** penalizes directional misalignment
+
+In fresh categories, bias is critical because:
+
+* persistent positive bias inflates waste
+* persistent negative bias triggers stockouts
+* even moderate bias forces operational replanning
+
+Two models with identical MAE may have **very different operational impact** if one produces drift and the other does not.
+
+This scoring function explicitly enforces **directional stability**.
+
+---
+
+## **5. Technical Findings (FreshNet)**
+
+The portfolio-level ranking (lower = more stable) shows a clear hierarchy:
+
+| Stability Tier                           | Model Families (ranked best → worst)                                              |
+| ---------------------------------------- | --------------------------------------------------------------------------------- |
+| **Tier A — Stable Models**               | *DynamicOptimizedTheta, SES/Holt/Holt-Winters, Chronos2, Theta, Croston variants* |
+| **Tier B — Acceptable Secondary Models** | *WindowAverage, HistoricAverage*                                                  |
+| **Tier C — High-Noise Models (avoid)**   | *LightGBM (without drivers), Naive, Drift, RandomWalk*                            |
+
+### Key outcomes:
+
+* **Smoothing-based models** consistently produced the most stable forecasts.
+* **Chronos2** performed strongly across mixed or noisy SKU profiles.
+* **Croston variants** excelled in zero-heavy SKUs.
+* **LightGBM**, without drivers such as discount, weather, and availability, became unstable in fresh environments.
+
+This finding aligns directly with fresh-category physics:
+**signal smoothing outperforms signal chasing**.
+
+---
+
+## **6. Technical Interpretation**
+
+![Portfolio-Level Model Ranking](./model_score_ranking.png)
+
+From the plot:
+
+* **Tier-A models** (left cluster) maintain a **stable slope**, **low bias**, and **consistent weekly direction**.
+* **Tier-B models** are usable but may oscillate under moderate volatility.
+* **Tier-C models** amplify noise and introduce unnecessary churn.
+
+### Why smoothing wins in FreshNet:
+
+* fresh demand is noisy; smoothing reduces false volatility
+* Theta/SES capture level shifts without reacting to every spike
+* Croston handles sparse and intermittent signals correctly
+* Chronos2 handles mixed structure without overshooting
+* ML methods require external drivers; without them, they overfit recent noise
+
+In short:
+
+> **Fresh demand rewards stability, not complexity.**
+
+---
+
+## **7. Deployment Standard**
+
+Based on FreshNet performance:
+
+> **Adopt Theta/SES/Holt-Winters as the default forecasting signal across FreshNet SKUs.**
+> **Use Croston variants for intermittent SKUs.**
+> **Use Chronos2 for variable SKUs with mixed seasonal or trend structure.**
+
+ML (LightGBM) becomes a **Phase-2 upgrade** once driver data is introduced:
+discount, stockout hours, weather, and availability ratios.
+
+---
+
+## **8. What This Resolves for Planning Teams**
+
+A stable forecast signal resolves several persistent operational issues:
+
+### Before
+
+* weekly overrides normalized
+* churn in order recommendations
+* ambiguous ownership of changes
+* poor alignment between stores, DCs, and planners
+
+### After
+
+* overrides become exception-based
+* decisions persist across cycles
+* signal changes correlate with real conditions
+* planners spend less time “fixing the number” and more time acting on it
+
+This standard **stabilizes the execution posture** across the fresh network.
+
+---
+
+## **9. Defensibility Properties**
+
+The system is technically defensible because:
+
+* evaluation is performed **per SKU**, not at the portfolio mean
+* stability scoring is **mathematically transparent**
+* all steps are deterministic, auditable, and logged
+* model selection follows **explicit rules**, not subjective preference
+* the plot provides **visual governance evidence**
+
+This creates a **repeatable, explainable, non-heuristic** model-selection framework.
+
+---
+
+## **Closing Position**
+
+Forecast Sandbox v1.0 is not a model shootout.
+
+It is a **decision pipeline** engineered to identify forecasting methods that produce a **stable, low-noise signal**,
+because stability—not marginal accuracy—determines operational reliability in fresh retail.
+
+By anchoring on smoothing models (Theta/SES/Holt) and augmenting with Croston and Chronos2 where needed,
+the resulting forecast becomes not only accurate but **operationally trustworthy**.
+
+**This stability is what reduces waste, improves store ordering, and increases execution confidence.**
+
+---
+

metrics/best_by_sku.csv

@@ -0,0 +1,298 @@
+,id,best_model,mae,bias,score,ADI,CV2,regime
+0,CID0_SID0_PID104_MGID6_CAT120-CAT250-CAT32,Holt,74.1446480896852,14.968214139932837,89.11286222961805,1.0,0.1971764236476327,Low-Low
+1,CID0_SID0_PID117_MGID6_CAT14-CAT228-CAT31,Holt,106.64931429751567,-8.760258754927106,115.4095730524428,1.0,0.12408092424692828,Low-Low
+2,CID0_SID0_PID118_MGID6_CAT14-CAT228-CAT3180,SeasonalExponentialSmoothingOptimized,37.389925839195385,-2.731973377354848,40.12189921655023,1.0,0.23694922135685326,Low-Low
+3,CID0_SID0_PID122_MGID6_CAT120-CAT268-CAT3127,HoltWinters,81.26502324370593,-56.31527439405344,137.58029763775937,1.0,0.2550312482584942,Low-Low
+4,CID0_SID0_PID127_MGID6_CAT120-CAT258-CAT3172,chronos2,21.079993111746653,-15.380333491734095,36.46032660348075,1.0133333333333334,0.20483547525780468,Low-Low
+5,CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181,CrostonOptimized,48.57142857142857,0.5674566205284017,49.13888519195697,1.4074074074074074,1.3106477168274608,High-High
+6,CID0_SID0_PID136_MGID6_CAT14-CAT228-CAT3161,WindowAverage,26.428571428571427,-8.214285714285714,34.64285714285714,1.0410958904109588,0.2739750026208807,Low-Low
+7,CID0_SID0_PID166_MGID6_CAT120-CAT250-CAT359,HistoricAverage,59.23684210526316,-16.0639097744361,75.30075187969926,1.0,0.2087847682367124,Low-Low
+8,CID0_SID0_PID18_MGID6_CAT14-CAT228-CAT3142,lightgbm,35.97933235679165,-23.89936122292039,59.87869357971204,1.0410958904109588,0.46391081833419723,Low-Low
+9,CID0_SID0_PID190_MGID6_CAT14-CAT228-CAT3131,CrostonClassic,49.34204825931458,-6.268597479029579,55.61064573834416,1.027027027027027,0.15692764032677128,Low-Low
+10,CID0_SID0_PID193_MGID6_CAT18-CAT229-CAT3114,SimpleExponentialSmoothingOptimized,15.279162929739426,-0.1887166346811538,15.46787956442058,1.0410958904109588,0.2337625867611395,Low-Low
+11,CID0_SID0_PID194_MGID5_CAT118-CAT280-CAT3109,WindowAverage,30.571428571428573,-2.2857142857142856,32.85714285714286,1.027027027027027,0.3521189710195946,Low-Low
+12,CID0_SID0_PID19_MGID6_CAT14-CAT228-CAT381,OptimizedTheta,34.4193010224814,0.1891462181826568,34.608447240664056,1.027027027027027,0.2484962450767301,Low-Low
+13,CID0_SID0_PID201_MGID6_CAT18-CAT229-CAT3113,OptimizedTheta,32.541003875379324,0.4964945223322701,33.037498397711595,1.0857142857142856,0.2991690389866705,Low-Low
+14,CID0_SID0_PID207_MGID6_CAT14-CAT228-CAT3179,HoltWinters,30.02445657460643,-7.498327887385399,37.52278446199183,1.0133333333333334,0.23843252429464612,Low-Low
+15,CID0_SID0_PID214_MGID6_CAT18-CAT28-CAT39,OptimizedTheta,14.072852247485752,5.621404147953186,19.69425639543893,1.0,0.22215235183252854,Low-Low
+16,CID0_SID0_PID215_MGID6_CAT14-CAT228-CAT3149,CrostonOptimized,62.40927697920618,0.2779182884138192,62.687195267620005,1.0,0.19521880787453225,Low-Low
+17,CID0_SID0_PID21_MGID6_CAT14-CAT228-CAT381,CrostonSBA,25.97889516295552,1.8522661406885987,27.831161303644112,1.0133333333333334,0.28952380952380957,Low-Low
+18,CID0_SID0_PID223_MGID6_CAT120-CAT258-CAT3172,Theta,34.05936716481999,0.193423878962065,34.252791043782054,1.0,0.13315566425015282,Low-Low
+19,CID0_SID0_PID23_MGID6_CAT14-CAT253-CAT358,HistoricAverage,56.49248120300752,-38.95864661654135,95.45112781954889,1.0555555555555556,0.4680149599439178,Low-Low
+20,CID0_SID0_PID253_MGID6_CAT110-CAT233-CAT383,HistoricAverage,15.661654135338347,4.112781954887216,19.774436090225564,1.0,0.23074852142612345,Low-Low
+21,CID0_SID0_PID259_MGID6_CAT121-CAT264-CAT3123,Naive,24.285714285714285,10.0,34.285714285714285,1.0704225352112675,0.32512224104586784,Low-Low
+22,CID0_SID0_PID26_MGID6_CAT14-CAT253-CAT3156,SeasonalNaive,22.571428571428573,-5.0,27.571428571428573,1.0133333333333334,0.1550563231828679,Low-Low
+23,CID0_SID0_PID291_MGID5_CAT116-CAT227-CAT398,RandomWalkWithDrift,23.5952380952381,0.8428571428571493,24.438095238095247,1.0133333333333334,0.19668899684678132,Low-Low
+24,CID0_SID0_PID296_MGID5_CAT116-CAT225-CAT3103,CrostonClassic,33.357142857142854,3.6570394431168034,37.014182300259655,1.0,0.13007091954530992,Low-Low
+25,CID0_SID0_PID300_MGID6_CAT120-CAT250-CAT324,SeasonalExponentialSmoothingOptimized,158.20233485149535,-22.34610408391797,180.5484389354133,1.0410958904109588,0.1906766445695131,Low-Low
+26,CID0_SID0_PID310_MGID5_CAT116-CAT225-CAT3105,HoltWinters,41.11604391360738,3.287712763836097,44.40375667744348,1.0,0.14497932837827804,Low-Low
+27,CID0_SID0_PID345_MGID5_CAT116-CAT226-CAT396,SimpleExponentialSmoothingOptimized,10.72955312891474,0.1068719024031636,10.836425031317903,1.027027027027027,0.32985206911360265,Low-Low
+28,CID0_SID0_PID370_MGID6_CAT124-CAT266-CAT3199,HistoricAverage,21.428571428571423,1.1654135338345952,22.59398496240602,1.0,0.18477370357185222,Low-Low
+29,CID0_SID0_PID379_MGID2_CAT129-CAT276-CAT3231,Naive,18.571428571428573,4.285714285714286,22.857142857142858,1.0857142857142856,0.6809988074242043,Low-High
+30,CID0_SID0_PID38_MGID0_CAT15-CAT26-CAT365,HistoricAverage,59.06015037593985,0.1127819548872146,59.17293233082707,1.2666666666666666,3.6246266995693626,Low-High
+31,CID0_SID0_PID411_MGID0_CAT128-CAT272-CAT3218,lightgbm,25.663213654982,2.871267545921826,28.534481200903823,1.3333333333333333,1.1612622951647007,High-High
+32,CID0_SID0_PID419_MGID6_CAT124-CAT251-CAT3153,lightgbm,16.780414324686227,-2.087943997851512,18.868358322537738,1.0,0.3909897795738385,Low-Low
+33,CID0_SID0_PID41_MGID6_CAT14-CAT253-CAT377,WindowAverage,66.42857142857143,0.7142857142857143,67.14285714285714,1.2063492063492063,0.6875146277792781,Low-High
+34,CID0_SID0_PID439_MGID6_CAT121-CAT261-CAT316,HistoricAverage,34.45488721804511,-11.672932330827072,46.12781954887218,1.2258064516129032,1.394487916245615,Low-High
+35,CID0_SID0_PID486_MGID6_CAT120-CAT268-CAT363,Naive,55.71428571428572,34.2857142857143,90.00000000000003,1.0,0.17688551830765015,Low-Low
+36,CID0_SID0_PID489_MGID6_CAT121-CAT264-CAT3123,Theta,25.87123872393056,0.1321586302636624,26.003397354194217,1.027027027027027,0.31868047943112776,Low-Low
+37,CID0_SID0_PID496_MGID5_CAT116-CAT225-CAT3101,SeasonalExponentialSmoothingOptimized,19.3126633235542,4.158897100607804,23.471560424162007,1.0,0.18767417798162353,Low-Low
+38,CID0_SID0_PID4_MGID2_CAT129-CAT278-CAT382,RandomWalkWithDrift,76.51428571428572,-14.85714285714286,91.37142857142858,1.0133333333333334,0.7608120244439397,Low-High
+39,CID0_SID0_PID500_MGID6_CAT14-CAT228-CAT3179,Holt,17.14368358592582,-0.9540405931948922,18.09772417912072,1.0,0.3976370391281715,Low-Low
+40,CID0_SID0_PID548_MGID3_CAT111-CAT262-CAT3182,HoltWinters,15.65737376505663,0.0550086170070816,15.71238238206371,1.0133333333333334,0.29182841152263383,Low-Low
+41,CID0_SID0_PID554_MGID6_CAT14-CAT228-CAT3168,Holt,31.06565172564761,-6.243647160492728,37.30929888614034,1.0704225352112675,0.3821215848316082,Low-Low
+42,CID0_SID0_PID567_MGID6_CAT120-CAT250-CAT359,lightgbm,35.65193896562161,-3.250050208211252,38.90198917383286,1.0,0.1097411047619048,Low-Low
+43,CID0_SID0_PID578_MGID6_CAT14-CAT253-CAT358,WindowAverage,22.857142857142858,-6.428571428571429,29.285714285714285,1.0,0.1927093726734022,Low-Low
+44,CID0_SID0_PID580_MGID2_CAT129-CAT276-CAT360,WindowAverage,43.57142857142857,5.0,48.57142857142857,1.0,0.14843451553164527,Low-Low
+45,CID0_SID0_PID596_MGID2_CAT129-CAT276-CAT360,WindowAverage,30.0,2.142857142857143,32.142857142857146,1.0,0.3470407159763036,Low-Low
+46,CID0_SID0_PID600_MGID2_CAT129-CAT278-CAT3157,Holt,62.93792792943303,-62.2348329473036,125.17276087673662,1.0133333333333334,0.14207492338200828,Low-Low
+47,CID0_SID0_PID631_MGID5_CAT116-CAT225-CAT3103,chronos2,22.781345094953263,-0.7412499019077846,23.52259499686105,1.0,0.16089171193235818,Low-Low
+48,CID0_SID0_PID633_MGID5_CAT116-CAT225-CAT3105,HoltWinters,12.320259615681854,0.6916668146335644,13.011926430315418,1.0133333333333334,0.25559400230680523,Low-Low
+49,CID0_SID0_PID635_MGID5_CAT116-CAT227-CAT3104,SimpleExponentialSmoothingOptimized,16.739579579343303,1.4627713411173895,18.20235092046069,1.027027027027027,0.472792026319116,Low-Low
+50,CID0_SID0_PID638_MGID5_CAT116-CAT225-CAT394,CrostonSBA,20.471690149300294,-0.269597526326516,20.74128767562681,1.0555555555555556,0.3172140861983451,Low-Low
+51,CID0_SID0_PID644_MGID4_CAT123-CAT210-CAT317,HistoricAverage,27.875939849624064,-15.996240601503766,43.87218045112783,1.027027027027027,0.453462004175141,Low-Low
+52,CID0_SID0_PID672_MGID6_CAT110-CAT238-CAT3173,WindowAverage,35.0,-2.142857142857143,37.142857142857146,1.0,0.28352621999728034,Low-Low
+53,CID0_SID0_PID686_MGID2_CAT10-CAT221-CAT3221,chronos2,9.82526125226702,0.0338145664760044,9.859075818743026,1.027027027027027,0.20480897084731808,Low-Low
+54,CID0_SID0_PID691_MGID6_CAT121-CAT264-CAT319,lightgbm,108.76366751754416,33.99500905303095,142.75867657057512,1.0,0.11757336572271444,Low-Low
+55,CID0_SID0_PID699_MGID2_CAT130-CAT275-CAT3191,SeasonalExponentialSmoothingOptimized,31.10891169553852,6.196572058535309,37.30548375407383,1.027027027027027,0.2696411649313415,Low-Low
+56,CID0_SID0_PID6_MGID6_CAT120-CAT250-CAT3170,WindowAverage,25.714285714285715,5.714285714285714,31.42857142857143,1.027027027027027,0.34285455288599676,Low-Low
+57,CID0_SID0_PID70_MGID6_CAT14-CAT228-CAT381,SeasonalExponentialSmoothingOptimized,103.821765670538,12.102737043795305,115.9245027143333,1.0133333333333334,0.16386533686007093,Low-Low
+58,CID0_SID0_PID712_MGID6_CAT124-CAT266-CAT3199,HistoricAverage,74.49248120300751,-1.44736842105263,75.93984962406014,1.027027027027027,0.21243169554700303,Low-Low
+59,CID0_SID0_PID719_MGID6_CAT110-CAT257-CAT3205,CrostonOptimized,12.142857142857142,1.0146631160645256,13.157520258921668,1.0133333333333334,0.20019461912620984,Low-Low
+60,CID0_SID0_PID72_MGID3_CAT125-CAT271-CAT3213,chronos2,17.24076243809291,0.4336934770856585,17.67445591517857,1.0133333333333334,0.27054461353010073,Low-Low
+61,CID0_SID0_PID740_MGID2_CAT129-CAT276-CAT360,SeasonalExponentialSmoothingOptimized,38.32526938903536,-0.2363012951534204,38.56157068418878,1.0133333333333334,0.1401843465288508,Low-Low
+62,CID0_SID0_PID764_MGID5_CAT118-CAT280-CAT3111,SimpleExponentialSmoothingOptimized,17.969954268717757,1.2182513095957346,19.188205578313493,1.0555555555555556,0.33632975083524524,Low-Low
+63,CID0_SID0_PID769_MGID6_CAT120-CAT268-CAT3176,chronos2,24.10213197980608,-3.310508183070592,27.41264016287668,1.0,0.17871501654974548,Low-Low
+64,CID0_SID0_PID76_MGID6_CAT18-CAT229-CAT3113,SimpleExponentialSmoothingOptimized,34.39596513324414,-0.7717559327089246,35.16772106595307,1.0410958904109588,0.26040833166836413,Low-Low
+65,CID0_SID0_PID775_MGID6_CAT14-CAT228-CAT3167,WindowAverage,41.42857142857143,5.357142857142855,46.785714285714285,1.0,0.13673047805306082,Low-Low
+66,CID0_SID0_PID783_MGID5_CAT122-CAT281-CAT356,Naive,37.142857142857146,4.285714285714286,41.42857142857143,1.027027027027027,0.2603139470413124,Low-Low
+67,CID0_SID0_PID796_MGID6_CAT14-CAT228-CAT31,Naive,52.85714285714285,-4.285714285714287,57.14285714285714,1.027027027027027,0.22365328681572352,Low-Low
+68,CID0_SID0_PID806_MGID6_CAT121-CAT264-CAT3184,WindowAverage,19.642857142857142,-3.2142857142857144,22.857142857142858,1.1014492753623188,0.46722449142604044,Low-Low
+69,CID0_SID0_PID810_MGID6_CAT14-CAT228-CAT3168,OptimizedTheta,52.91053889501601,-2.8536583226024663,55.76419721761848,1.0410958904109588,0.3311113345980873,Low-Low
+70,CID0_SID0_PID834_MGID0_CAT128-CAT272-CAT3154,SeasonalNaive,126.42857142857144,-107.85714285714286,234.28571428571428,1.0555555555555556,0.3329786902358695,Low-Low
+71,CID0_SID0_PID843_MGID6_CAT14-CAT228-CAT3167,SeasonalNaive,17.857142857142858,2.142857142857143,20.0,1.0133333333333334,0.26866505465572404,Low-Low
+72,CID0_SID0_PID90_MGID6_CAT14-CAT253-CAT377,SimpleExponentialSmoothingOptimized,86.54768990031718,-4.405257873648783,90.95294777396596,1.1692307692307693,0.6752129571358582,Low-High
+73,CID0_SID0_PID93_MGID6_CAT121-CAT261-CAT3223,Naive,27.142857142857142,0.0,27.142857142857142,1.0,0.14355505416505404,Low-Low
+74,CID0_SID12_PID129_MGID6_CAT110-CAT233-CAT3181,HistoricAverage,22.06766917293233,1.6165413533834578,23.684210526315788,1.4901960784313726,1.3379718263832971,High-High
+75,CID0_SID12_PID41_MGID6_CAT14-CAT253-CAT377,WindowAverage,22.142857142857142,-12.5,34.64285714285714,1.6888888888888889,1.631704385899762,High-High
+76,CID0_SID12_PID768_MGID0_CAT15-CAT25-CAT36,Holt,82.94153707028401,0.2969241424671369,83.23846121275115,2.0,2.213212992886328,High-High
+77,CID0_SID18_PID362_MGID1_CAT17-CAT217-CAT351,HoltWinters,25.613546114677604,4.996233459541671,30.609779574219274,1.5833333333333333,1.8282105632259478,High-High
+78,CID0_SID18_PID536_MGID0_CAT15-CAT26-CAT365,lightgbm,97.19304447404768,-33.63039548546906,130.82343995951675,1.4074074074074074,2.9096372144533245,High-High
+79,CID0_SID18_PID830_MGID1_CAT17-CAT216-CAT344,CrostonSBA,21.51375307147087,3.8695643215766062,25.38331739304748,1.3571428571428572,1.4335999999999998,High-High
+80,CID0_SID19_PID768_MGID0_CAT15-CAT25-CAT36,HoltWinters,30.08234890988777,-2.339070594172626,32.421419504060395,1.4615384615384615,1.5838267655287495,High-High
+81,CID0_SID1_PID104_MGID6_CAT120-CAT250-CAT32,Naive,107.14285714285714,5.714285714285714,112.85714285714285,1.0,0.22088805135802483,Low-Low
+82,CID0_SID1_PID108_MGID6_CAT18-CAT229-CAT3113,SeasonalExponentialSmoothingOptimized,36.16417549024652,-2.3985378840583387,38.56271337430486,1.0555555555555556,0.2925528999089149,Low-Low
+83,CID0_SID1_PID110_MGID2_CAT131-CAT279-CAT3121,HoltWinters,15.151830526698657,-1.843996332906612,16.99582685960527,1.0133333333333334,0.13414268711055097,Low-Low
+84,CID0_SID1_PID114_MGID6_CAT110-CAT233-CAT372,WindowAverage,91.78571428571428,-3.928571428571429,95.71428571428572,1.0704225352112675,0.24028346585152538,Low-Low
+85,CID0_SID1_PID117_MGID6_CAT14-CAT228-CAT31,HistoricAverage,181.42857142857144,12.857142857142849,194.28571428571428,1.0,0.15021312680196738,Low-Low
+86,CID0_SID1_PID118_MGID6_CAT14-CAT228-CAT3180,CrostonClassic,48.65533001095665,-2.269832780446334,50.92516279140299,1.0,0.1672784501135178,Low-Low
+87,CID0_SID1_PID121_MGID2_CAT130-CAT274-CAT3195,Naive,30.0,0.0,30.0,1.0133333333333334,0.6963980993030984,Low-High
+88,CID0_SID1_PID122_MGID6_CAT120-CAT268-CAT3127,HoltWinters,107.38672637849012,-87.75640672553871,195.14313310402883,1.0,0.24941294526139426,Low-Low
+89,CID0_SID1_PID127_MGID6_CAT120-CAT258-CAT3172,Theta,47.28211688946606,-3.489980133272805,50.77209702273886,1.0,0.186238614160185,Low-Low
+90,CID0_SID1_PID133_MGID2_CAT131-CAT277-CAT373,HoltWinters,26.31011162054695,-4.785922451057294,31.096034071604244,1.0857142857142856,0.6964287537016264,Low-High
+91,CID0_SID1_PID136_MGID6_CAT14-CAT228-CAT3161,Naive,25.0,2.142857142857143,27.142857142857142,1.0133333333333334,0.24392549494846955,Low-Low
+92,CID0_SID1_PID138_MGID4_CAT127-CAT237-CAT3126,WindowAverage,18.214285714285715,-4.642857142857143,22.857142857142858,1.0,0.1986394557823129,Low-Low
+93,CID0_SID1_PID140_MGID6_CAT14-CAT228-CAT310,CrostonClassic,16.80643489235802,1.9307585322204173,18.73719342457844,1.0133333333333334,0.26845168880685394,Low-Low
+94,CID0_SID1_PID151_MGID2_CAT131-CAT279-CAT3158,Naive,10.714285714285714,0.7142857142857143,11.428571428571429,1.027027027027027,0.14788333333333328,Low-Low
+95,CID0_SID1_PID166_MGID6_CAT120-CAT250-CAT359,chronos2,85.53926522391183,-2.8530338832310287,88.39229910714286,1.0,0.18731050812495323,Low-Low
+96,CID0_SID1_PID16_MGID6_CAT18-CAT229-CAT3113,chronos2,31.12902777535575,0.9658083234514508,32.0948360988072,1.0857142857142856,0.2835489462467762,Low-Low
+97,CID0_SID1_PID17_MGID6_CAT120-CAT258-CAT3172,lightgbm,16.875462018690452,3.566432786959264,20.44189480564972,1.0,0.15620948862163592,Low-Low
+98,CID0_SID1_PID18_MGID6_CAT14-CAT228-CAT3142,Holt,41.03607719741688,-27.71188559667483,68.7479627940917,1.027027027027027,0.4110036501882263,Low-Low
+99,CID0_SID1_PID190_MGID6_CAT14-CAT228-CAT3131,HistoricAverage,69.84962406015039,0.5263157894736565,70.37593984962405,1.027027027027027,0.13514015945534355,Low-Low
+100,CID0_SID1_PID194_MGID5_CAT118-CAT280-CAT3109,WindowAverage,70.14285714285714,-10.571428571428571,80.71428571428571,1.0133333333333334,0.4286481870871856,Low-Low
+101,CID0_SID1_PID19_MGID6_CAT14-CAT228-CAT381,OptimizedTheta,66.61251434799857,-22.434159907349105,89.04667425534768,1.0133333333333334,0.19537718407733265,Low-Low
+102,CID0_SID1_PID200_MGID6_CAT18-CAT229-CAT3113,WindowAverage,18.428571428571427,-2.0,20.428571428571427,1.0857142857142856,0.3057794898504011,Low-Low
+103,CID0_SID1_PID201_MGID6_CAT18-CAT229-CAT3113,Theta,27.924191069029835,-2.2862332618356027,30.21042433086544,1.0133333333333334,0.23114123420545468,Low-Low
+104,CID0_SID1_PID207_MGID6_CAT14-CAT228-CAT3179,Holt,47.12075405478341,-8.092242288500776,55.21299634328419,1.0133333333333334,0.20970560893842377,Low-Low
+105,CID0_SID1_PID213_MGID6_CAT14-CAT228-CAT381,CrostonClassic,15.440750225980064,-0.4861769895681241,15.926927215548186,1.0555555555555556,0.2989009718420747,Low-Low
+106,CID0_SID1_PID214_MGID6_CAT18-CAT28-CAT39,SeasonalExponentialSmoothingOptimized,18.554907125008373,-2.596491434553145,21.15139855956152,1.0,0.23471271711649216,Low-Low
+107,CID0_SID1_PID215_MGID6_CAT14-CAT228-CAT3149,chronos2,130.78228759765625,-15.932730538504464,146.71501813616072,1.0,0.19121460652674513,Low-Low
+108,CID0_SID1_PID216_MGID6_CAT121-CAT264-CAT3123,chronos2,17.586494990757533,-1.8066727774483813,19.39316776820592,1.0133333333333334,0.30913471967356326,Low-Low
+109,CID0_SID1_PID219_MGID6_CAT18-CAT229-CAT3114,chronos2,23.760510308401926,-1.5513428279331751,25.311853136335102,1.0704225352112675,0.2535510168109811,Low-Low
+110,CID0_SID1_PID220_MGID6_CAT18-CAT229-CAT3113,CrostonClassic,28.571428571428573,0.240431226472527,28.8118597979011,1.027027027027027,0.2735667895929337,Low-Low
+111,CID0_SID1_PID23_MGID6_CAT14-CAT253-CAT358,HistoricAverage,74.94172932330828,-45.91165413533834,120.85338345864662,1.0133333333333334,0.322010417718093,Low-Low
+112,CID0_SID1_PID247_MGID5_CAT116-CAT225-CAT394,Holt,26.13631032248044,3.516487673372871,29.65279799585331,1.0133333333333334,0.27461058563818047,Low-Low
+113,CID0_SID1_PID249_MGID5_CAT116-CAT225-CAT3103,RandomWalkWithDrift,16.076190476190476,7.571428571428574,23.64761904761905,1.0,0.1588919923957375,Low-Low
+114,CID0_SID1_PID250_MGID5_CAT116-CAT227-CAT397,DynamicOptimizedTheta,19.285714285714285,-2.592724146871443,21.878438432585728,1.0,0.21860037921996897,Low-Low
+115,CID0_SID1_PID258_MGID2_CAT131-CAT279-CAT3121,WindowAverage,20.714285714285715,0.7142857142857143,21.42857142857143,1.027027027027027,0.24205193608955966,Low-Low
+116,CID0_SID1_PID259_MGID6_CAT121-CAT264-CAT3123,Naive,39.285714285714285,9.285714285714286,48.57142857142857,1.0857142857142856,0.22715395925225787,Low-Low
+117,CID0_SID1_PID26_MGID6_CAT14-CAT253-CAT3156,HistoricAverage,35.92857142857143,0.075187969924806,36.00375939849624,1.0133333333333334,0.14258916696439816,Low-Low
+118,CID0_SID1_PID27_MGID6_CAT14-CAT228-CAT310,Theta,12.020496045308072,0.3289003179895159,12.34939636329759,1.0410958904109588,0.5177144891420432,Low-High
+119,CID0_SID1_PID285_MGID6_CAT124-CAT251-CAT3203,SeasonalExponentialSmoothingOptimized,22.29708324591197,0.9035097466470514,23.200592992559024,1.0410958904109588,0.31588619304586585,Low-Low
+120,CID0_SID1_PID290_MGID5_CAT116-CAT225-CAT3105,RandomWalkWithDrift,49.28000000000001,-11.885714285714316,61.16571428571432,1.0133333333333334,0.13778236647594325,Low-Low
+121,CID0_SID1_PID291_MGID5_CAT116-CAT227-CAT398,chronos2,34.63008989606585,16.35544150216239,50.98553139822823,1.0,0.13707794976915516,Low-Low
+122,CID0_SID1_PID292_MGID5_CAT116-CAT227-CAT3104,WindowAverage,53.57142857142857,13.571428571428571,67.14285714285714,1.0,0.12537103046037937,Low-Low
+123,CID0_SID1_PID293_MGID5_CAT116-CAT227-CAT3100,HistoricAverage,17.571428571428573,1.7857142857142858,19.357142857142858,1.0,0.22811113546156275,Low-Low
+124,CID0_SID1_PID295_MGID5_CAT116-CAT225-CAT3106,HoltWinters,20.86647137165124,-3.103933450019005,23.970404821670243,1.027027027027027,0.18705225372529524,Low-Low
+125,CID0_SID1_PID296_MGID5_CAT116-CAT225-CAT3103,CrostonSBA,43.25489734471574,-0.3585757298469372,43.61347307456268,1.0133333333333334,0.16025747741559024,Low-Low
+126,CID0_SID1_PID300_MGID6_CAT120-CAT250-CAT324,HoltWinters,284.13809817819833,-8.717823427076626,292.85592160527494,1.0,0.1488741614703775,Low-Low
+127,CID0_SID1_PID304_MGID2_CAT131-CAT279-CAT3227,HistoricAverage,12.556390977443607,-0.6766917293233112,13.23308270676692,1.0,0.2504633219165193,Low-Low
+128,CID0_SID1_PID321_MGID5_CAT118-CAT280-CAT3109,WindowAverage,21.0,-1.5714285714285714,22.571428571428573,1.0555555555555556,0.336112483589598,Low-Low
+129,CID0_SID1_PID345_MGID5_CAT116-CAT226-CAT396,HoltWinters,13.492038835841036,-2.991938701762319,16.483977537603355,1.0133333333333334,0.21447923361298443,Low-Low
+130,CID0_SID1_PID362_MGID1_CAT17-CAT217-CAT351,lightgbm,45.77494616873181,-31.85421679835535,77.62916296708715,1.1014492753623188,0.9981295135546113,Low-High
+131,CID0_SID1_PID366_MGID5_CAT116-CAT225-CAT3103,lightgbm,18.12391862070587,2.237053485271141,20.36097210597701,1.0133333333333334,0.20481052091974913,Low-Low
+132,CID0_SID1_PID368_MGID5_CAT118-CAT280-CAT3112,WindowAverage,15.0,-1.0714285714285714,16.071428571428573,1.0133333333333334,0.3358937268027689,Low-Low
+133,CID0_SID1_PID370_MGID6_CAT124-CAT266-CAT3199,RandomWalkWithDrift,41.21904761904762,-5.142857142857143,46.36190476190477,1.0,0.1489024526748971,Low-Low
+134,CID0_SID1_PID373_MGID2_CAT131-CAT279-CAT322,HistoricAverage,27.96992481203008,-4.680451127819546,32.650375939849624,1.0704225352112675,0.4040159761923408,Low-Low
+135,CID0_SID1_PID374_MGID5_CAT118-CAT245-CAT393,HoltWinters,9.756191492005431,-1.269861789509806,11.026053281515235,1.0133333333333334,0.1306655577446161,Low-Low
+136,CID0_SID1_PID379_MGID2_CAT129-CAT276-CAT3231,RandomWalkWithDrift,19.65714285714285,-4.0,23.65714285714285,1.0410958904109588,0.7594566694805625,Low-High
+137,CID0_SID1_PID381_MGID0_CAT128-CAT23-CAT37,SimpleExponentialSmoothingOptimized,49.02600817910298,36.33122859914452,85.3572367782475,1.0704225352112675,0.22387400954043904,Low-Low
+138,CID0_SID1_PID38_MGID0_CAT15-CAT26-CAT365,chronos2,72.80022185189384,-35.320929118565154,108.12115097045898,1.0410958904109588,2.6851145936966616,Low-High
+139,CID0_SID1_PID411_MGID0_CAT128-CAT272-CAT3218,Naive,40.0,-1.4285714285714286,41.42857142857143,1.0857142857142856,0.6735660807291668,Low-High
+140,CID0_SID1_PID415_MGID1_CAT17-CAT217-CAT342,lightgbm,24.110243843909554,7.995375985517865,32.10561982942742,1.0,0.19125292951144773,Low-Low
+141,CID0_SID1_PID422_MGID5_CAT116-CAT225-CAT3101,CrostonSBA,33.79631808355545,2.215684721015504,36.012002804570955,1.0,0.5652119858719501,Low-High
+142,CID0_SID1_PID424_MGID5_CAT116-CAT227-CAT397,CrostonClassic,11.640182444207117,-0.0527056808783774,11.692888125085494,1.027027027027027,0.8099333100483599,Low-High
+143,CID0_SID1_PID439_MGID6_CAT121-CAT261-CAT316,Holt,40.33501145231434,-1.903099598638293,42.23811105095263,1.0410958904109588,1.5275745850748288,Low-High
+144,CID0_SID1_PID452_MGID2_CAT129-CAT276-CAT360,WindowAverage,36.785714285714285,1.7857142857142858,38.57142857142857,1.027027027027027,0.4193027650589105,Low-Low
+145,CID0_SID1_PID470_MGID5_CAT116-CAT225-CAT3101,OptimizedTheta,11.072042363270995,0.6200975055337258,11.692139868804723,1.0133333333333334,0.22844721651103525,Low-Low
+146,CID0_SID1_PID486_MGID6_CAT120-CAT268-CAT363,CrostonSBA,72.44933836854185,-0.0025114369358029,72.45184980547765,1.0,0.18612898284313717,Low-Low
+147,CID0_SID1_PID487_MGID3_CAT111-CAT262-CAT3182,lightgbm,45.25908503889261,-42.84714962859767,88.10623466749028,1.0555555555555556,0.341277119649265,Low-Low
+148,CID0_SID1_PID489_MGID6_CAT121-CAT264-CAT3123,HistoricAverage,55.78947368421053,-4.191729323308266,59.98120300751879,1.0,0.16863100923710395,Low-Low
+149,CID0_SID1_PID496_MGID5_CAT116-CAT225-CAT3101,DynamicTheta,19.685056831210805,-0.0821760643207412,19.767232895531546,1.0,0.1944138816999707,Low-Low
+150,CID0_SID1_PID499_MGID6_CAT120-CAT268-CAT318,HoltWinters,13.689659798887476,-2.032124719128676,15.721784518016152,1.0133333333333334,0.21928746996213055,Low-Low
+151,CID0_SID1_PID4_MGID2_CAT129-CAT278-CAT382,Holt,99.57007542029,-19.542798628578687,119.11287404886868,1.0,0.7260427341597798,Low-High
+152,CID0_SID1_PID500_MGID6_CAT14-CAT228-CAT3179,HoltWinters,22.57947241619524,1.1354172050901212,23.714889621285355,1.0,0.44673288755488444,Low-Low
+153,CID0_SID1_PID554_MGID6_CAT14-CAT228-CAT3168,HoltWinters,47.28102786341327,-0.4042071468928847,47.68523501030616,1.027027027027027,0.21930025712657344,Low-Low
+154,CID0_SID1_PID556_MGID5_CAT116-CAT225-CAT394,CrostonSBA,13.886182877610583,6.210617190615172,20.09680006822576,1.0133333333333334,1.6383303143742711,Low-High
+155,CID0_SID1_PID563_MGID5_CAT116-CAT225-CAT3105,HistoricAverage,15.571428571428571,-4.035714285714286,19.607142857142858,1.027027027027027,1.0917945805072007,Low-High
+156,CID0_SID1_PID578_MGID6_CAT14-CAT253-CAT358,RandomWalkWithDrift,28.057142857142857,-9.714285714285715,37.77142857142857,1.0,0.0957481292683724,Low-Low
+157,CID0_SID1_PID58_MGID5_CAT115-CAT242-CAT389,HistoricAverage,27.94924812030075,17.73872180451128,45.68796992481203,1.0,0.2249671386329506,Low-Low
+158,CID0_SID1_PID592_MGID5_CAT115-CAT243-CAT311,HistoricAverage,22.18045112781955,-4.154135338345869,26.33458646616542,1.027027027027027,0.24074506448850633,Low-Low
+159,CID0_SID1_PID596_MGID2_CAT129-CAT276-CAT360,chronos2,53.45385524204799,-3.3035169328962053,56.7573721749442,1.0,0.2947165610277279,Low-Low
+160,CID0_SID1_PID60_MGID6_CAT121-CAT261-CAT3224,HoltWinters,19.77765357177695,-0.0562527127632935,19.83390628454024,1.0,0.2012420256991687,Low-Low
+161,CID0_SID1_PID622_MGID6_CAT121-CAT261-CAT3178,CrostonSBA,27.86979446453844,-1.7414336598297413,29.611228124368186,1.0133333333333334,0.27372513942004906,Low-Low
+162,CID0_SID1_PID627_MGID4_CAT113-CAT21-CAT3214,HistoricAverage,19.285714285714285,4.285714285714286,23.57142857142857,1.0410958904109588,0.523456790123457,Low-High
+163,CID0_SID1_PID628_MGID6_CAT121-CAT261-CAT316,lightgbm,50.30055872996915,-35.04358082387715,85.3441395538463,1.027027027027027,1.6910020062708946,Low-High
+164,CID0_SID1_PID62_MGID0_CAT128-CAT252-CAT361,HoltWinters,32.78034452477277,-14.056822342107996,46.83716686688077,1.1014492753623188,0.4943970471994921,Low-High
+165,CID0_SID1_PID631_MGID5_CAT116-CAT225-CAT3103,HistoricAverage,26.101503759398497,7.28947368421052,33.39097744360902,1.0,0.13599812076766019,Low-Low
+166,CID0_SID1_PID633_MGID5_CAT116-CAT225-CAT3105,SeasonalExponentialSmoothingOptimized,12.140867125287892,-0.297030822011228,12.43789794729912,1.0,0.21241982558428488,Low-Low
+167,CID0_SID1_PID634_MGID5_CAT116-CAT225-CAT399,Holt,14.609573874210977,0.6840621903794967,15.293636064590473,1.0,0.17112285927643214,Low-Low
+168,CID0_SID1_PID635_MGID5_CAT116-CAT227-CAT3104,HoltWinters,19.9106020843305,1.0632351605172576,20.97383724484776,1.027027027027027,0.24074547094497983,Low-Low
+169,CID0_SID1_PID636_MGID5_CAT116-CAT225-CAT3106,lightgbm,12.290998145667109,-0.0231351260622693,12.314133271729377,1.0133333333333334,0.23878890832062905,Low-Low
+170,CID0_SID1_PID638_MGID5_CAT116-CAT225-CAT394,SeasonalNaive,20.714285714285715,-0.7142857142857143,21.42857142857143,1.0,0.2262132862304545,Low-Low
+171,CID0_SID1_PID63_MGID0_CAT128-CAT252-CAT3169,WindowAverage,40.71428571428572,-22.857142857142858,63.57142857142857,1.3333333333333333,0.9985675374686365,High-High
+172,CID0_SID1_PID644_MGID4_CAT123-CAT210-CAT317,DynamicTheta,26.483036243352338,2.979733634862697,29.462769878215035,1.0,0.3932398004951737,Low-Low
+173,CID0_SID1_PID653_MGID1_CAT17-CAT217-CAT354,RandomWalkWithDrift,19.16,-10.342857142857136,29.50285714285713,1.0410958904109588,0.24526958062463342,Low-Low
+174,CID0_SID1_PID663_MGID6_CAT110-CAT233-CAT3186,chronos2,10.398132732936316,-0.0156463895525251,10.41377912248884,1.0133333333333334,0.3324929169137312,Low-Low
+175,CID0_SID1_PID670_MGID2_CAT131-CAT279-CAT3227,HoltWinters,24.35920022026841,-13.521317509080728,37.88051772934914,1.0133333333333334,0.17037037037037037,Low-Low
+176,CID0_SID1_PID686_MGID2_CAT10-CAT221-CAT3221,SeasonalExponentialSmoothingOptimized,29.895091740329573,-0.7899402779373149,30.685032018266888,1.027027027027027,0.24043682698877694,Low-Low
+177,CID0_SID1_PID691_MGID6_CAT121-CAT264-CAT319,WindowAverage,242.8571428571429,-82.85714285714286,325.7142857142857,1.0133333333333334,0.11754650695862867,Low-Low
+178,CID0_SID1_PID6_MGID6_CAT120-CAT250-CAT3170,chronos2,53.31753540039063,-2.617542811802457,55.93507821219308,1.0133333333333334,0.38672765360112293,Low-Low
+179,CID0_SID1_PID706_MGID4_CAT127-CAT20-CAT30,WindowAverage,4.285714285714286,0.0,4.285714285714286,1.0,0.10658041172116992,Low-Low
+180,CID0_SID1_PID70_MGID6_CAT14-CAT228-CAT381,OptimizedTheta,148.40973642672944,3.646377079635662,152.0561135063651,1.027027027027027,0.15424410480996956,Low-Low
+181,CID0_SID1_PID711_MGID4_CAT127-CAT237-CAT3126,HistoricAverage,17.857142857142858,3.026315789473685,20.883458646616543,1.0410958904109588,0.38617336585976103,Low-Low
+182,CID0_SID1_PID717_MGID5_CAT116-CAT225-CAT3103,SimpleExponentialSmoothingOptimized,17.894526230043702,-0.4526021039798057,18.34712833402351,1.027027027027027,0.3153086533311772,Low-Low
+183,CID0_SID1_PID719_MGID6_CAT110-CAT257-CAT3205,WindowAverage,16.785714285714285,1.0714285714285714,17.857142857142858,1.0,0.16281908245694254,Low-Low
+184,CID0_SID1_PID738_MGID6_CAT14-CAT228-CAT381,RandomWalkWithDrift,18.23809523809524,-3.571428571428572,21.80952380952381,1.0133333333333334,0.2575160563717362,Low-Low
+185,CID0_SID1_PID740_MGID2_CAT129-CAT276-CAT360,SeasonalExponentialSmoothingOptimized,41.348114152310856,-3.660118279010893,45.00823243132175,1.0,0.11734531201832032,Low-Low
+186,CID0_SID1_PID74_MGID6_CAT121-CAT261-CAT316,HoltWinters,8.666673939415025,2.9807652078785885,11.647439147293614,1.0,0.1541014309254015,Low-Low
+187,CID0_SID1_PID764_MGID5_CAT118-CAT280-CAT3111,WindowAverage,22.5,-0.3571428571428571,22.857142857142858,1.0133333333333334,0.2379621603657805,Low-Low
+188,CID0_SID1_PID765_MGID5_CAT118-CAT280-CAT3112,lightgbm,16.665370963510902,0.3448704015189468,17.01024136502985,1.0,0.18138708832090833,Low-Low
+189,CID0_SID1_PID768_MGID0_CAT15-CAT25-CAT36,SeasonalExponentialSmoothingOptimized,46.04064319155067,-4.336266775570465,50.376909967121136,1.1875,0.8368193257616732,Low-High
+190,CID0_SID1_PID769_MGID6_CAT120-CAT268-CAT3176,HoltWinters,32.018586880418525,10.34559156192917,42.36417844234769,1.0,0.1276644723727689,Low-Low
+191,CID0_SID1_PID76_MGID6_CAT18-CAT229-CAT3113,RandomWalkWithDrift,71.0,0.7142857142857143,71.71428571428571,1.0555555555555556,0.2461119349700665,Low-Low
+192,CID0_SID1_PID775_MGID6_CAT14-CAT228-CAT3167,Theta,59.26555151333589,0.3312839890385776,59.59683550237447,1.0,0.15649054684206593,Low-Low
+193,CID0_SID1_PID783_MGID5_CAT122-CAT281-CAT356,OptimizedTheta,42.57097952290589,-6.277992408614874,48.848971931520765,1.0410958904109588,0.3282181637368697,Low-Low
+194,CID0_SID1_PID793_MGID3_CAT114-CAT240-CAT3146,chronos2,39.77202933175223,-5.101170131138393,44.87319946289063,1.1692307692307693,0.5936307037767157,Low-High
+195,CID0_SID1_PID796_MGID6_CAT14-CAT228-CAT31,DynamicTheta,111.35465409014698,1.712452715148698,113.06710680529568,1.027027027027027,0.1567955745273326,Low-Low
+196,CID0_SID1_PID79_MGID6_CAT14-CAT228-CAT310,lightgbm,10.752592875471018,-1.1841096804773394,11.936702555948358,1.0,0.34435237352625625,Low-Low
+197,CID0_SID1_PID7_MGID2_CAT131-CAT279-CAT3228,HistoricAverage,11.428571428571429,0.9962406015037584,12.424812030075188,1.0555555555555556,0.24857925014877996,Low-Low
+198,CID0_SID1_PID806_MGID6_CAT121-CAT264-CAT3184,WindowAverage,24.285714285714285,-2.857142857142857,27.142857142857142,1.1014492753623188,0.4378600823045268,Low-Low
+199,CID0_SID1_PID810_MGID6_CAT14-CAT228-CAT3168,WindowAverage,47.142857142857146,2.142857142857143,49.28571428571429,1.0410958904109588,0.282601810790175,Low-Low
+200,CID0_SID1_PID816_MGID4_CAT127-CAT20-CAT30,CrostonClassic,10.537066380827644,-1.6039879685450231,12.141054349372666,1.0,0.08783246113224091,Low-Low
+201,CID0_SID1_PID822_MGID6_CAT120-CAT250-CAT3183,CrostonSBA,30.67594019651509,0.4458670898913287,31.12180728640642,1.0857142857142856,0.37275872135077603,Low-Low
+202,CID0_SID1_PID829_MGID3_CAT111-CAT235-CAT371,chronos2,13.020542825971331,-3.832501956394741,16.853044782366073,1.0410958904109588,0.4001819941055478,Low-Low
+203,CID0_SID1_PID830_MGID1_CAT17-CAT216-CAT344,CrostonClassic,26.42857142857144,-4.625413755209165,31.0539851837806,1.2666666666666666,1.673658972982644,Low-High
+204,CID0_SID1_PID834_MGID0_CAT128-CAT272-CAT3154,RandomWalkWithDrift,116.95238095238096,-76.2857142857143,193.23809523809527,1.0410958904109588,0.26050244405410455,Low-Low
+205,CID0_SID1_PID841_MGID2_CAT10-CAT213-CAT327,Naive,12.857142857142858,1.4285714285714286,14.285714285714286,1.0133333333333334,0.2547837560997681,Low-Low
+206,CID0_SID1_PID843_MGID6_CAT14-CAT228-CAT3167,SeasonalExponentialSmoothingOptimized,26.08135381504448,-2.46783920599582,28.5491930210403,1.0133333333333334,0.12502009592899668,Low-Low
+207,CID0_SID1_PID847_MGID6_CAT120-CAT250-CAT3229,Holt,15.072163553601351,-3.004629665819178,18.07679321942053,1.0,0.27891471036667614,Low-Low
+208,CID0_SID1_PID858_MGID5_CAT115-CAT243-CAT311,Naive,10.714285714285714,-2.142857142857143,12.857142857142856,1.027027027027027,2.371932876581431,Low-High
+209,CID0_SID1_PID90_MGID6_CAT14-CAT253-CAT377,WindowAverage,113.57142857142856,-15.357142857142858,128.92857142857142,1.1692307692307693,0.8244375836434904,Low-High
+210,CID0_SID1_PID94_MGID6_CAT14-CAT228-CAT3216,WindowAverage,30.0,-11.428571428571429,41.42857142857143,1.0133333333333334,0.19319998458395968,Low-Low
+211,CID0_SID1_PID99_MGID6_CAT18-CAT229-CAT3115,WindowAverage,33.57142857142857,-13.214285714285714,46.785714285714285,1.0133333333333334,0.2523763095057699,Low-Low
+212,CID0_SID1_PID9_MGID2_CAT131-CAT279-CAT3232,SimpleExponentialSmoothingOptimized,19.427493183539266,-2.6390832852445567,22.06657646878382,1.027027027027027,0.19094983111111108,Low-Low
+213,CID0_SID2_PID104_MGID6_CAT120-CAT250-CAT32,HoltWinters,54.63820730480289,0.8647721476746304,55.50297945247752,1.0,0.13475865687587057,Low-Low
+214,CID0_SID2_PID115_MGID6_CAT110-CAT233-CAT3181,SeasonalExponentialSmoothingOptimized,19.4752965787653,-4.619005853392285,24.094302432157583,1.1176470588235294,0.528266655166666,Low-High
+215,CID0_SID2_PID118_MGID6_CAT14-CAT228-CAT3180,SeasonalExponentialSmoothingOptimized,27.79538420794287,0.430361826140178,28.22574603408305,1.0133333333333334,0.21388164995689715,Low-Low
+216,CID0_SID2_PID11_MGID5_CAT118-CAT280-CAT386,DynamicOptimizedTheta,12.142857142857142,-0.0588115745824698,12.201668717439611,1.0133333333333334,0.1923335047171021,Low-Low
+217,CID0_SID2_PID122_MGID6_CAT120-CAT268-CAT3127,OptimizedTheta,82.41259288344945,-61.95276487452424,144.3653577579737,1.0,0.16264076055416424,Low-Low
+218,CID0_SID2_PID127_MGID6_CAT120-CAT258-CAT3172,chronos2,26.71504810878209,-0.0697299412318638,26.78477805001395,1.0410958904109588,0.22279323396927905,Low-Low
+219,CID0_SID2_PID129_MGID6_CAT110-CAT233-CAT3181,SeasonalExponentialSmoothingOptimized,34.87654915704686,-1.808011031766132,36.684560188813,1.3103448275862069,1.275422524106523,Low-High
+220,CID0_SID2_PID12_MGID5_CAT118-CAT280-CAT386,WindowAverage,13.928571428571429,-6.071428571428571,20.0,1.0133333333333334,0.3044229369451241,Low-Low
+221,CID0_SID2_PID135_MGID6_CAT14-CAT228-CAT3167,Theta,13.50357142857143,0.0424733183610785,13.546044746932507,1.0,0.23096581117395373,Low-Low
+222,CID0_SID2_PID136_MGID6_CAT14-CAT228-CAT3161,WindowAverage,8.571428571428571,-1.4285714285714286,10.0,1.0555555555555556,0.2701644070086933,Low-Low
+223,CID0_SID2_PID140_MGID6_CAT14-CAT228-CAT310,Holt,19.07533532715331,-0.7531145799651462,19.828449907118458,1.0410958904109588,0.2540828402366865,Low-Low
+224,CID0_SID2_PID145_MGID5_CAT118-CAT280-CAT3110,CrostonSBA,11.26037851940758,-0.2315323249306123,11.491910844338191,1.0,0.18543534760492064,Low-Low
+225,CID0_SID2_PID150_MGID6_CAT18-CAT229-CAT3116,SeasonalExponentialSmoothingOptimized,26.55319553651263,-16.47523342757108,43.02842896408371,1.027027027027027,0.28429157705182717,Low-Low
+226,CID0_SID2_PID16_MGID6_CAT18-CAT229-CAT3113,Holt,11.254634692163489,3.7738595658595,15.028494258022986,1.027027027027027,0.2559409208247559,Low-Low
+227,CID0_SID2_PID181_MGID5_CAT118-CAT280-CAT3112,SimpleExponentialSmoothingOptimized,22.3117987648,0.4683056393143029,22.7801044041143,1.0133333333333334,0.23123283349035975,Low-Low
+228,CID0_SID2_PID193_MGID6_CAT18-CAT229-CAT3114,CrostonSBA,23.57452155247673,-1.0606031377600138,24.635124690236747,1.0133333333333334,0.1141934805134538,Low-Low
+229,CID0_SID2_PID194_MGID5_CAT118-CAT280-CAT3109,Holt,35.469611585882284,-0.8410324581480749,36.31064404403036,1.0410958904109588,0.359576401622178,Low-Low
+230,CID0_SID2_PID19_MGID6_CAT14-CAT228-CAT381,HistoricAverage,41.8421052631579,-2.124060150375941,43.96616541353384,1.0133333333333334,0.14643393024311502,Low-Low
+231,CID0_SID2_PID200_MGID6_CAT18-CAT229-CAT3113,HistoricAverage,16.842105263157894,-1.3909774436090256,18.23308270676692,1.0555555555555556,0.2834460489412797,Low-Low
+232,CID0_SID2_PID201_MGID6_CAT18-CAT229-CAT3113,OptimizedTheta,23.68084860158425,0.2982575477132556,23.979106149297504,1.0,0.16340810207475953,Low-Low
+233,CID0_SID2_PID207_MGID6_CAT14-CAT228-CAT3179,HoltWinters,37.40488270048122,1.4252022126610078,38.83008491314222,1.0133333333333334,0.1453144722873225,Low-Low
+234,CID0_SID2_PID212_MGID6_CAT120-CAT250-CAT3229,OptimizedTheta,21.519799349292665,0.4724272650995585,21.992226614392223,1.0133333333333334,0.1641292168460166,Low-Low
+235,CID0_SID2_PID213_MGID6_CAT14-CAT228-CAT381,DynamicOptimizedTheta,15.010182782094304,0.0119030980743468,15.022085880168651,1.0133333333333334,0.38912518639733584,Low-Low
+236,CID0_SID2_PID215_MGID6_CAT14-CAT228-CAT3149,SimpleExponentialSmoothingOptimized,49.556278047661486,-1.677482237798147,51.23376028545963,1.0,0.09361045498467357,Low-Low
+237,CID0_SID2_PID216_MGID6_CAT121-CAT264-CAT3123,CrostonSBA,27.857142857142858,0.4875910203018817,28.34473387744474,1.027027027027027,0.15248960016257684,Low-Low
+238,CID0_SID2_PID21_MGID6_CAT14-CAT228-CAT381,CrostonClassic,28.391283706826723,1.7019355975350428,30.093219304361767,1.0133333333333334,0.20183170875009732,Low-Low
+239,CID0_SID2_PID220_MGID6_CAT18-CAT229-CAT3113,Holt,9.999781889175608,-1.0937263417592848,11.093508230934892,1.027027027027027,0.2199607665610383,Low-Low
+240,CID0_SID2_PID223_MGID6_CAT120-CAT258-CAT3172,HistoricAverage,31.80451127819549,7.368421052631589,39.17293233082708,1.0,0.15521872080966823,Low-Low
+241,CID0_SID2_PID23_MGID6_CAT14-CAT253-CAT358,RandomWalkWithDrift,36.285714285714285,-19.71428571428572,56.0,1.0,0.29787316140839604,Low-Low
+242,CID0_SID2_PID240_MGID6_CAT14-CAT228-CAT3180,Holt,52.61575447493538,-34.65429072507295,87.27004520000833,1.0133333333333334,0.15596955017301045,Low-Low
+243,CID0_SID2_PID250_MGID5_CAT116-CAT227-CAT397,RandomWalkWithDrift,14.333333333333334,-0.7142857142857137,15.047619047619047,1.0,0.21267308405774435,Low-Low
+244,CID0_SID2_PID26_MGID6_CAT14-CAT253-CAT3156,CrostonClassic,17.349536384777,-2.1519487752909305,19.50148516006793,1.0133333333333334,0.24522453707334116,Low-Low
+245,CID0_SID2_PID27_MGID6_CAT14-CAT228-CAT310,DynamicOptimizedTheta,10.771452085017968,-1.114450309411487,11.885902394429454,1.0410958904109588,0.6557574476112293,Low-High
+246,CID0_SID2_PID290_MGID5_CAT116-CAT225-CAT3105,Naive,36.71428571428572,-0.1428571428571428,36.85714285714286,1.0,0.11992923071855355,Low-Low
+247,CID0_SID2_PID291_MGID5_CAT116-CAT227-CAT398,HoltWinters,21.71842462805137,1.4507702310575417,23.16919485910892,1.0,0.19542553891483985,Low-Low
+248,CID0_SID2_PID292_MGID5_CAT116-CAT227-CAT3104,chronos2,35.43684714181082,27.313066755022323,62.74991389683315,1.0,0.10980231226936639,Low-Low
+249,CID0_SID2_PID300_MGID6_CAT120-CAT250-CAT324,RandomWalkWithDrift,148.17142857142855,-45.14285714285715,193.3142857142857,1.0133333333333334,0.16549840824533388,Low-Low
+250,CID0_SID2_PID321_MGID5_CAT118-CAT280-CAT3109,HistoricAverage,15.714285714285714,2.5357142857142856,18.25,1.0704225352112675,0.28575903854642787,Low-Low
+251,CID0_SID2_PID345_MGID5_CAT116-CAT226-CAT396,SeasonalExponentialSmoothingOptimized,17.287960563484358,-0.3600267209296827,17.64798728441404,1.0,0.1740011580471672,Low-Low
+252,CID0_SID2_PID363_MGID6_CAT14-CAT253-CAT377,CrostonSBA,35.30757760323721,4.647950182758821,39.95552778599603,1.0704225352112675,0.252975460659524,Low-Low
+253,CID0_SID2_PID379_MGID2_CAT129-CAT276-CAT3231,SeasonalNaive,25.714285714285715,5.714285714285714,31.42857142857143,1.0857142857142856,0.5817600517562717,Low-High
+254,CID0_SID2_PID41_MGID6_CAT14-CAT253-CAT377,HistoricAverage,48.796992481203006,-34.14473684210526,82.94172932330827,1.1875,0.7770893814098333,Low-High
+255,CID0_SID2_PID422_MGID5_CAT116-CAT225-CAT3101,WindowAverage,21.071428571428573,-1.0714285714285714,22.142857142857142,1.0,0.9453605096038615,Low-High
+256,CID0_SID2_PID452_MGID2_CAT129-CAT276-CAT360,WindowAverage,20.714285714285715,-0.3571428571428571,21.071428571428573,1.0555555555555556,0.2961812530967075,Low-Low
+257,CID0_SID2_PID473_MGID1_CAT17-CAT217-CAT346,HoltWinters,14.93687337676925,2.5308285490074303,17.46770192577668,1.027027027027027,0.2252583490863265,Low-Low
+258,CID0_SID2_PID486_MGID6_CAT120-CAT268-CAT363,CrostonSBA,35.6224176097496,27.89274734840929,63.51516495815889,1.0,0.1715574492957842,Low-Low
+259,CID0_SID2_PID496_MGID5_CAT116-CAT225-CAT3101,Holt,19.795832132959585,-5.562539923050094,25.35837205600968,1.0,0.16972525328384058,Low-Low
+260,CID0_SID2_PID4_MGID2_CAT129-CAT278-CAT382,Naive,83.57142857142857,0.7142857142857143,84.28571428571428,1.0,0.6782498151252919,Low-High
+261,CID0_SID2_PID500_MGID6_CAT14-CAT228-CAT3179,chronos2,14.71936525617327,-1.6552848815917969,16.374650137765066,1.0,0.5977393310265281,Low-High
+262,CID0_SID2_PID549_MGID2_CAT131-CAT279-CAT3230,CrostonClassic,32.82539421726811,0.4682630967044576,33.29365731397257,1.027027027027027,0.20857982766304378,Low-Low
+263,CID0_SID2_PID554_MGID6_CAT14-CAT228-CAT3168,Naive,27.857142857142858,3.571428571428572,31.42857142857143,1.0410958904109588,0.21581636945587565,Low-Low
+264,CID0_SID2_PID556_MGID5_CAT116-CAT225-CAT394,OptimizedTheta,12.2799582954029,2.912335509727456,15.192293805130356,1.027027027027027,0.5242784640428996,Low-High
+265,CID0_SID2_PID567_MGID6_CAT120-CAT250-CAT359,HoltWinters,19.024203816244192,-0.966220178331302,19.99042399457549,1.0133333333333334,0.2063896583564174,Low-Low
+266,CID0_SID2_PID578_MGID6_CAT14-CAT253-CAT358,CrostonSBA,27.50785376738525,1.7307379140175894,29.23859168140284,1.0133333333333334,0.16527432468993414,Low-Low
+267,CID0_SID2_PID580_MGID2_CAT129-CAT276-CAT360,DynamicOptimizedTheta,73.75209396180672,10.989471723466377,84.7415656852731,1.0,0.12866929822729353,Low-Low
+268,CID0_SID2_PID58_MGID5_CAT115-CAT242-CAT389,CrostonSBA,18.20046337231062,-0.04837819691285,18.24884156922347,1.027027027027027,0.2532986974118287,Low-Low
+269,CID0_SID2_PID600_MGID2_CAT129-CAT278-CAT3157,WindowAverage,77.14285714285714,-65.71428571428571,142.85714285714283,1.0,0.11742395833333331,Low-Low
+270,CID0_SID2_PID633_MGID5_CAT116-CAT225-CAT3105,CrostonSBA,12.98749193726111,8.399385948847348,21.38687788610845,1.0133333333333334,0.17822968240550663,Low-Low
+271,CID0_SID2_PID634_MGID5_CAT116-CAT225-CAT399,WindowAverage,8.214285714285714,-3.928571428571429,12.142857142857142,1.0,0.1716083412518484,Low-Low
+272,CID0_SID2_PID635_MGID5_CAT116-CAT227-CAT3104,WindowAverage,15.0,0.0,15.0,1.0133333333333334,0.3069294749036346,Low-Low
+273,CID0_SID2_PID638_MGID5_CAT116-CAT225-CAT394,Holt,18.36576576022726,2.2808146193880314,20.64658037961529,1.0133333333333334,0.20903489330561215,Low-Low
+274,CID0_SID2_PID644_MGID4_CAT123-CAT210-CAT317,CrostonClassic,24.088308774036697,0.380509280557009,24.468818054593708,1.0133333333333334,0.37374009471640224,Low-Low
+275,CID0_SID2_PID645_MGID6_CAT124-CAT269-CAT3139,WindowAverage,42.0,-8.0,50.0,1.0133333333333334,0.9029339152603421,Low-High
+276,CID0_SID2_PID650_MGID6_CAT110-CAT233-CAT3160,CrostonClassic,21.428571428571423,0.0934907079329333,21.52206213650436,1.1875,0.49135157854855216,Low-High
+277,CID0_SID2_PID670_MGID2_CAT131-CAT279-CAT3227,HoltWinters,20.128835273316124,0.635264549762954,20.76409982307908,1.0,0.15441621546577822,Low-Low
+278,CID0_SID2_PID686_MGID2_CAT10-CAT221-CAT3221,chronos2,11.988862446376256,1.949472972324916,13.938335418701172,1.027027027027027,0.3462660521962538,Low-Low
+279,CID0_SID2_PID691_MGID6_CAT121-CAT264-CAT319,WindowAverage,120.0,-12.857142857142858,132.85714285714286,1.0,0.10621241365220954,Low-Low
+280,CID0_SID2_PID6_MGID6_CAT120-CAT250-CAT3170,HoltWinters,36.86519969485119,-1.7428289988349153,38.6080286936861,1.0,0.23566834324035982,Low-Low
+281,CID0_SID2_PID70_MGID6_CAT14-CAT228-CAT381,chronos2,85.5773446219308,-5.505000523158482,91.08234514508928,1.0,0.12412841401718615,Low-Low
+282,CID0_SID2_PID715_MGID6_CAT110-CAT238-CAT3190,CrostonClassic,34.68060358084632,-2.189316038466447,36.86991961931277,1.0410958904109588,0.29375315005818,Low-Low
+283,CID0_SID2_PID719_MGID6_CAT110-CAT257-CAT3205,WindowAverage,17.357142857142858,0.2142857142857142,17.571428571428573,1.0,0.13300867298853516,Low-Low
+284,CID0_SID2_PID728_MGID6_CAT120-CAT258-CAT374,Holt,21.46753176368835,-5.435068041454154,26.902599805142504,1.0,0.25466849565615013,Low-Low
+285,CID0_SID2_PID76_MGID6_CAT18-CAT229-CAT3113,Holt,43.54310996780045,-3.3986330273782235,46.94174299517867,1.027027027027027,0.13153549189047054,Low-Low
+286,CID0_SID2_PID774_MGID6_CAT14-CAT253-CAT377,Naive,36.42857142857143,3.571428571428572,40.0,1.0704225352112675,0.32277395558669764,Low-Low
+287,CID0_SID2_PID775_MGID6_CAT14-CAT228-CAT3167,HistoricAverage,64.4360902255639,2.4812030075187983,66.9172932330827,1.0,0.08089451014206295,Low-Low
+288,CID0_SID2_PID77_MGID6_CAT18-CAT229-CAT3113,chronos2,24.279197147914346,0.2114731924874442,24.490670340401785,1.027027027027027,0.25020156079891764,Low-Low
+289,CID0_SID2_PID783_MGID5_CAT122-CAT281-CAT356,WindowAverage,38.92857142857143,0.3571428571428571,39.285714285714285,1.027027027027027,0.20726504282059827,Low-Low
+290,CID0_SID2_PID802_MGID0_CAT15-CAT27-CAT366,HistoricAverage,66.74812030075189,-12.951127819548883,79.69924812030078,1.1515151515151516,0.9831995273154898,Low-High
+291,CID0_SID2_PID829_MGID3_CAT111-CAT235-CAT371,Naive,21.428571428571427,8.571428571428571,30.0,1.0555555555555556,0.31338283705603315,Low-Low
+292,CID0_SID2_PID834_MGID0_CAT128-CAT272-CAT3154,Holt,54.711867319381966,-37.956205148918585,92.66807246830056,1.0410958904109588,0.287243384359796,Low-Low
+293,CID0_SID2_PID843_MGID6_CAT14-CAT228-CAT3167,lightgbm,16.72842873599509,-2.229648804294808,18.9580775402899,1.1176470588235294,0.308266069392358,Low-Low
+294,CID0_SID2_PID93_MGID6_CAT121-CAT261-CAT3223,Theta,16.598306805975977,-0.042550410171021,16.640857216146998,1.0,0.13540114747481877,Low-Low
+295,CID0_SID8_PID411_MGID0_CAT128-CAT272-CAT3218,Holt,39.34180909139396,-0.4776973866143724,39.819506478008336,1.3103448275862069,0.9827265306122449,Low-High
+296,CID0_SID8_PID90_MGID6_CAT14-CAT253-CAT377,SeasonalExponentialSmoothingOptimized,45.79869826459655,12.41364429640701,58.21234256100355,1.6170212765957446,1.669801631812169,High-High

metrics/best_model_overall.csv

@@ -0,0 +1,19 @@
+model,score
+DynamicOptimizedTheta,66.88939869784375
+SimpleExponentialSmoothingOptimized,67.3129073682663
+chronos2,67.65465826848312
+Theta,67.68086751999303
+DynamicTheta,67.6855328161418
+CrostonOptimized,67.88342278631056
+CrostonClassic,68.3568407404228
+OptimizedTheta,68.59058296080063
+CrostonSBA,70.98925614766407
+SeasonalExponentialSmoothingOptimized,71.39687050348432
+HoltWinters,71.83911579989166
+WindowAverage,73.38443963443964
+Holt,74.04482645712783
+SeasonalNaive,76.74025974025975
+lightgbm,83.91043096968025
+HistoricAverage,84.07320067846383
+Naive,88.82972582972583
+RandomWalkWithDrift,92.74065736732403

metrics/best_models.csv

@@ -0,0 +1,298 @@
+id,best_model,mae,bias,score
+CID0_SID0_PID104_MGID6_CAT120-CAT250-CAT32,Holt,74.1446480896852,14.968214139932837,89.11286222961805
+CID0_SID0_PID117_MGID6_CAT14-CAT228-CAT31,Holt,106.64931429751567,-8.760258754927106,115.4095730524428
+CID0_SID0_PID118_MGID6_CAT14-CAT228-CAT3180,SeasonalExponentialSmoothingOptimized,37.389925839195385,-2.731973377354848,40.12189921655023
+CID0_SID0_PID122_MGID6_CAT120-CAT268-CAT3127,HoltWinters,81.26502324370593,-56.31527439405344,137.58029763775937
+CID0_SID0_PID127_MGID6_CAT120-CAT258-CAT3172,chronos2,21.079993111746653,-15.380333491734095,36.46032660348075
+CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181,CrostonOptimized,48.57142857142857,0.5674566205284017,49.13888519195697
+CID0_SID0_PID136_MGID6_CAT14-CAT228-CAT3161,WindowAverage,26.428571428571427,-8.214285714285714,34.64285714285714
+CID0_SID0_PID166_MGID6_CAT120-CAT250-CAT359,HistoricAverage,59.23684210526316,-16.0639097744361,75.30075187969926
+CID0_SID0_PID18_MGID6_CAT14-CAT228-CAT3142,lightgbm,35.97933235679165,-23.89936122292039,59.87869357971204
+CID0_SID0_PID190_MGID6_CAT14-CAT228-CAT3131,CrostonClassic,49.34204825931458,-6.268597479029579,55.61064573834416
+CID0_SID0_PID193_MGID6_CAT18-CAT229-CAT3114,SimpleExponentialSmoothingOptimized,15.279162929739426,-0.1887166346811538,15.46787956442058
+CID0_SID0_PID194_MGID5_CAT118-CAT280-CAT3109,WindowAverage,30.571428571428573,-2.2857142857142856,32.85714285714286
+CID0_SID0_PID19_MGID6_CAT14-CAT228-CAT381,OptimizedTheta,34.4193010224814,0.1891462181826568,34.608447240664056
+CID0_SID0_PID201_MGID6_CAT18-CAT229-CAT3113,OptimizedTheta,32.541003875379324,0.4964945223322701,33.037498397711595
+CID0_SID0_PID207_MGID6_CAT14-CAT228-CAT3179,HoltWinters,30.02445657460643,-7.498327887385399,37.52278446199183
+CID0_SID0_PID214_MGID6_CAT18-CAT28-CAT39,OptimizedTheta,14.072852247485752,5.621404147953186,19.69425639543893
+CID0_SID0_PID215_MGID6_CAT14-CAT228-CAT3149,CrostonOptimized,62.40927697920618,0.2779182884138192,62.687195267620005
+CID0_SID0_PID21_MGID6_CAT14-CAT228-CAT381,CrostonSBA,25.97889516295552,1.8522661406885987,27.831161303644112
+CID0_SID0_PID223_MGID6_CAT120-CAT258-CAT3172,Theta,34.05936716481999,0.193423878962065,34.252791043782054
+CID0_SID0_PID23_MGID6_CAT14-CAT253-CAT358,HistoricAverage,56.49248120300752,-38.95864661654135,95.45112781954889
+CID0_SID0_PID253_MGID6_CAT110-CAT233-CAT383,HistoricAverage,15.661654135338347,4.112781954887216,19.774436090225564
+CID0_SID0_PID259_MGID6_CAT121-CAT264-CAT3123,Naive,24.285714285714285,10.0,34.285714285714285
+CID0_SID0_PID26_MGID6_CAT14-CAT253-CAT3156,SeasonalNaive,22.571428571428573,-5.0,27.571428571428573
+CID0_SID0_PID291_MGID5_CAT116-CAT227-CAT398,RandomWalkWithDrift,23.5952380952381,0.8428571428571493,24.438095238095247
+CID0_SID0_PID296_MGID5_CAT116-CAT225-CAT3103,CrostonClassic,33.357142857142854,3.6570394431168034,37.014182300259655
+CID0_SID0_PID300_MGID6_CAT120-CAT250-CAT324,SeasonalExponentialSmoothingOptimized,158.20233485149535,-22.34610408391797,180.5484389354133
+CID0_SID0_PID310_MGID5_CAT116-CAT225-CAT3105,HoltWinters,41.11604391360738,3.287712763836097,44.40375667744348
+CID0_SID0_PID345_MGID5_CAT116-CAT226-CAT396,SimpleExponentialSmoothingOptimized,10.72955312891474,0.1068719024031636,10.836425031317903
+CID0_SID0_PID370_MGID6_CAT124-CAT266-CAT3199,HistoricAverage,21.428571428571423,1.1654135338345952,22.59398496240602
+CID0_SID0_PID379_MGID2_CAT129-CAT276-CAT3231,Naive,18.571428571428573,4.285714285714286,22.857142857142858
+CID0_SID0_PID38_MGID0_CAT15-CAT26-CAT365,HistoricAverage,59.06015037593985,0.1127819548872146,59.17293233082707
+CID0_SID0_PID411_MGID0_CAT128-CAT272-CAT3218,lightgbm,25.663213654982,2.871267545921826,28.534481200903823
+CID0_SID0_PID419_MGID6_CAT124-CAT251-CAT3153,lightgbm,16.780414324686227,-2.087943997851512,18.868358322537738
+CID0_SID0_PID41_MGID6_CAT14-CAT253-CAT377,WindowAverage,66.42857142857143,0.7142857142857143,67.14285714285714
+CID0_SID0_PID439_MGID6_CAT121-CAT261-CAT316,HistoricAverage,34.45488721804511,-11.672932330827072,46.12781954887218
+CID0_SID0_PID486_MGID6_CAT120-CAT268-CAT363,Naive,55.71428571428572,34.2857142857143,90.00000000000003
+CID0_SID0_PID489_MGID6_CAT121-CAT264-CAT3123,Theta,25.87123872393056,0.1321586302636624,26.003397354194217
+CID0_SID0_PID496_MGID5_CAT116-CAT225-CAT3101,SeasonalExponentialSmoothingOptimized,19.3126633235542,4.158897100607804,23.471560424162007
+CID0_SID0_PID4_MGID2_CAT129-CAT278-CAT382,RandomWalkWithDrift,76.51428571428572,-14.85714285714286,91.37142857142858
+CID0_SID0_PID500_MGID6_CAT14-CAT228-CAT3179,Holt,17.14368358592582,-0.9540405931948922,18.09772417912072
+CID0_SID0_PID548_MGID3_CAT111-CAT262-CAT3182,HoltWinters,15.65737376505663,0.0550086170070816,15.71238238206371
+CID0_SID0_PID554_MGID6_CAT14-CAT228-CAT3168,Holt,31.06565172564761,-6.243647160492728,37.30929888614034
+CID0_SID0_PID567_MGID6_CAT120-CAT250-CAT359,lightgbm,35.65193896562161,-3.250050208211252,38.90198917383286
+CID0_SID0_PID578_MGID6_CAT14-CAT253-CAT358,WindowAverage,22.857142857142858,-6.428571428571429,29.285714285714285
+CID0_SID0_PID580_MGID2_CAT129-CAT276-CAT360,WindowAverage,43.57142857142857,5.0,48.57142857142857
+CID0_SID0_PID596_MGID2_CAT129-CAT276-CAT360,WindowAverage,30.0,2.142857142857143,32.142857142857146
+CID0_SID0_PID600_MGID2_CAT129-CAT278-CAT3157,Holt,62.93792792943303,-62.2348329473036,125.17276087673662
+CID0_SID0_PID631_MGID5_CAT116-CAT225-CAT3103,chronos2,22.781345094953263,-0.7412499019077846,23.52259499686105
+CID0_SID0_PID633_MGID5_CAT116-CAT225-CAT3105,HoltWinters,12.320259615681854,0.6916668146335644,13.011926430315418
+CID0_SID0_PID635_MGID5_CAT116-CAT227-CAT3104,SimpleExponentialSmoothingOptimized,16.739579579343303,1.4627713411173895,18.20235092046069
+CID0_SID0_PID638_MGID5_CAT116-CAT225-CAT394,CrostonSBA,20.471690149300294,-0.269597526326516,20.74128767562681
+CID0_SID0_PID644_MGID4_CAT123-CAT210-CAT317,HistoricAverage,27.875939849624064,-15.996240601503766,43.87218045112783
+CID0_SID0_PID672_MGID6_CAT110-CAT238-CAT3173,WindowAverage,35.0,-2.142857142857143,37.142857142857146
+CID0_SID0_PID686_MGID2_CAT10-CAT221-CAT3221,chronos2,9.82526125226702,0.0338145664760044,9.859075818743026
+CID0_SID0_PID691_MGID6_CAT121-CAT264-CAT319,lightgbm,108.76366751754416,33.99500905303095,142.75867657057512
+CID0_SID0_PID699_MGID2_CAT130-CAT275-CAT3191,SeasonalExponentialSmoothingOptimized,31.10891169553852,6.196572058535309,37.30548375407383
+CID0_SID0_PID6_MGID6_CAT120-CAT250-CAT3170,WindowAverage,25.714285714285715,5.714285714285714,31.42857142857143
+CID0_SID0_PID70_MGID6_CAT14-CAT228-CAT381,SeasonalExponentialSmoothingOptimized,103.821765670538,12.102737043795305,115.9245027143333
+CID0_SID0_PID712_MGID6_CAT124-CAT266-CAT3199,HistoricAverage,74.49248120300751,-1.44736842105263,75.93984962406014
+CID0_SID0_PID719_MGID6_CAT110-CAT257-CAT3205,CrostonOptimized,12.142857142857142,1.0146631160645256,13.157520258921668
+CID0_SID0_PID72_MGID3_CAT125-CAT271-CAT3213,chronos2,17.24076243809291,0.4336934770856585,17.67445591517857
+CID0_SID0_PID740_MGID2_CAT129-CAT276-CAT360,SeasonalExponentialSmoothingOptimized,38.32526938903536,-0.2363012951534204,38.56157068418878
+CID0_SID0_PID764_MGID5_CAT118-CAT280-CAT3111,SimpleExponentialSmoothingOptimized,17.969954268717757,1.2182513095957346,19.188205578313493
+CID0_SID0_PID769_MGID6_CAT120-CAT268-CAT3176,chronos2,24.10213197980608,-3.310508183070592,27.41264016287668
+CID0_SID0_PID76_MGID6_CAT18-CAT229-CAT3113,SimpleExponentialSmoothingOptimized,34.39596513324414,-0.7717559327089246,35.16772106595307
+CID0_SID0_PID775_MGID6_CAT14-CAT228-CAT3167,WindowAverage,41.42857142857143,5.357142857142855,46.785714285714285
+CID0_SID0_PID783_MGID5_CAT122-CAT281-CAT356,Naive,37.142857142857146,4.285714285714286,41.42857142857143
+CID0_SID0_PID796_MGID6_CAT14-CAT228-CAT31,Naive,52.85714285714285,-4.285714285714287,57.14285714285714
+CID0_SID0_PID806_MGID6_CAT121-CAT264-CAT3184,WindowAverage,19.642857142857142,-3.2142857142857144,22.857142857142858
+CID0_SID0_PID810_MGID6_CAT14-CAT228-CAT3168,OptimizedTheta,52.91053889501601,-2.8536583226024663,55.76419721761848
+CID0_SID0_PID834_MGID0_CAT128-CAT272-CAT3154,SeasonalNaive,126.42857142857144,-107.85714285714286,234.28571428571428
+CID0_SID0_PID843_MGID6_CAT14-CAT228-CAT3167,SeasonalNaive,17.857142857142858,2.142857142857143,20.0
+CID0_SID0_PID90_MGID6_CAT14-CAT253-CAT377,SimpleExponentialSmoothingOptimized,86.54768990031718,-4.405257873648783,90.95294777396596
+CID0_SID0_PID93_MGID6_CAT121-CAT261-CAT3223,Naive,27.142857142857142,0.0,27.142857142857142
+CID0_SID12_PID129_MGID6_CAT110-CAT233-CAT3181,HistoricAverage,22.06766917293233,1.6165413533834578,23.684210526315788
+CID0_SID12_PID41_MGID6_CAT14-CAT253-CAT377,WindowAverage,22.142857142857142,-12.5,34.64285714285714
+CID0_SID12_PID768_MGID0_CAT15-CAT25-CAT36,Holt,82.94153707028401,0.2969241424671369,83.23846121275115
+CID0_SID18_PID362_MGID1_CAT17-CAT217-CAT351,HoltWinters,25.613546114677604,4.996233459541671,30.609779574219274
+CID0_SID18_PID536_MGID0_CAT15-CAT26-CAT365,lightgbm,97.19304447404768,-33.63039548546906,130.82343995951675
+CID0_SID18_PID830_MGID1_CAT17-CAT216-CAT344,CrostonSBA,21.51375307147087,3.8695643215766062,25.38331739304748
+CID0_SID19_PID768_MGID0_CAT15-CAT25-CAT36,HoltWinters,30.08234890988777,-2.339070594172626,32.421419504060395
+CID0_SID1_PID104_MGID6_CAT120-CAT250-CAT32,Naive,107.14285714285714,5.714285714285714,112.85714285714285
+CID0_SID1_PID108_MGID6_CAT18-CAT229-CAT3113,SeasonalExponentialSmoothingOptimized,36.16417549024652,-2.3985378840583387,38.56271337430486
+CID0_SID1_PID110_MGID2_CAT131-CAT279-CAT3121,HoltWinters,15.151830526698657,-1.843996332906612,16.99582685960527
+CID0_SID1_PID114_MGID6_CAT110-CAT233-CAT372,WindowAverage,91.78571428571428,-3.928571428571429,95.71428571428572
+CID0_SID1_PID117_MGID6_CAT14-CAT228-CAT31,HistoricAverage,181.42857142857144,12.857142857142849,194.28571428571428
+CID0_SID1_PID118_MGID6_CAT14-CAT228-CAT3180,CrostonClassic,48.65533001095665,-2.269832780446334,50.92516279140299
+CID0_SID1_PID121_MGID2_CAT130-CAT274-CAT3195,Naive,30.0,0.0,30.0
+CID0_SID1_PID122_MGID6_CAT120-CAT268-CAT3127,HoltWinters,107.38672637849012,-87.75640672553871,195.14313310402883
+CID0_SID1_PID127_MGID6_CAT120-CAT258-CAT3172,Theta,47.28211688946606,-3.489980133272805,50.77209702273886
+CID0_SID1_PID133_MGID2_CAT131-CAT277-CAT373,HoltWinters,26.31011162054695,-4.785922451057294,31.096034071604244
+CID0_SID1_PID136_MGID6_CAT14-CAT228-CAT3161,Naive,25.0,2.142857142857143,27.142857142857142
+CID0_SID1_PID138_MGID4_CAT127-CAT237-CAT3126,WindowAverage,18.214285714285715,-4.642857142857143,22.857142857142858
+CID0_SID1_PID140_MGID6_CAT14-CAT228-CAT310,CrostonClassic,16.80643489235802,1.9307585322204173,18.73719342457844
+CID0_SID1_PID151_MGID2_CAT131-CAT279-CAT3158,Naive,10.714285714285714,0.7142857142857143,11.428571428571429
+CID0_SID1_PID166_MGID6_CAT120-CAT250-CAT359,chronos2,85.53926522391183,-2.8530338832310287,88.39229910714286
+CID0_SID1_PID16_MGID6_CAT18-CAT229-CAT3113,chronos2,31.12902777535575,0.9658083234514508,32.0948360988072
+CID0_SID1_PID17_MGID6_CAT120-CAT258-CAT3172,lightgbm,16.875462018690452,3.566432786959264,20.44189480564972
+CID0_SID1_PID18_MGID6_CAT14-CAT228-CAT3142,Holt,41.03607719741688,-27.71188559667483,68.7479627940917
+CID0_SID1_PID190_MGID6_CAT14-CAT228-CAT3131,HistoricAverage,69.84962406015039,0.5263157894736565,70.37593984962405
+CID0_SID1_PID194_MGID5_CAT118-CAT280-CAT3109,WindowAverage,70.14285714285714,-10.571428571428571,80.71428571428571
+CID0_SID1_PID19_MGID6_CAT14-CAT228-CAT381,OptimizedTheta,66.61251434799857,-22.434159907349105,89.04667425534768
+CID0_SID1_PID200_MGID6_CAT18-CAT229-CAT3113,WindowAverage,18.428571428571427,-2.0,20.428571428571427
+CID0_SID1_PID201_MGID6_CAT18-CAT229-CAT3113,Theta,27.924191069029835,-2.2862332618356027,30.21042433086544
+CID0_SID1_PID207_MGID6_CAT14-CAT228-CAT3179,Holt,47.12075405478341,-8.092242288500776,55.21299634328419
+CID0_SID1_PID213_MGID6_CAT14-CAT228-CAT381,CrostonClassic,15.440750225980064,-0.4861769895681241,15.926927215548186
+CID0_SID1_PID214_MGID6_CAT18-CAT28-CAT39,SeasonalExponentialSmoothingOptimized,18.554907125008373,-2.596491434553145,21.15139855956152
+CID0_SID1_PID215_MGID6_CAT14-CAT228-CAT3149,chronos2,130.78228759765625,-15.932730538504464,146.71501813616072
+CID0_SID1_PID216_MGID6_CAT121-CAT264-CAT3123,chronos2,17.586494990757533,-1.8066727774483813,19.39316776820592
+CID0_SID1_PID219_MGID6_CAT18-CAT229-CAT3114,chronos2,23.760510308401926,-1.5513428279331751,25.311853136335102
+CID0_SID1_PID220_MGID6_CAT18-CAT229-CAT3113,CrostonClassic,28.571428571428573,0.240431226472527,28.8118597979011
+CID0_SID1_PID23_MGID6_CAT14-CAT253-CAT358,HistoricAverage,74.94172932330828,-45.91165413533834,120.85338345864662
+CID0_SID1_PID247_MGID5_CAT116-CAT225-CAT394,Holt,26.13631032248044,3.516487673372871,29.65279799585331
+CID0_SID1_PID249_MGID5_CAT116-CAT225-CAT3103,RandomWalkWithDrift,16.076190476190476,7.571428571428574,23.64761904761905
+CID0_SID1_PID250_MGID5_CAT116-CAT227-CAT397,DynamicOptimizedTheta,19.285714285714285,-2.592724146871443,21.878438432585728
+CID0_SID1_PID258_MGID2_CAT131-CAT279-CAT3121,WindowAverage,20.714285714285715,0.7142857142857143,21.42857142857143
+CID0_SID1_PID259_MGID6_CAT121-CAT264-CAT3123,Naive,39.285714285714285,9.285714285714286,48.57142857142857
+CID0_SID1_PID26_MGID6_CAT14-CAT253-CAT3156,HistoricAverage,35.92857142857143,0.075187969924806,36.00375939849624
+CID0_SID1_PID27_MGID6_CAT14-CAT228-CAT310,Theta,12.020496045308072,0.3289003179895159,12.34939636329759
+CID0_SID1_PID285_MGID6_CAT124-CAT251-CAT3203,SeasonalExponentialSmoothingOptimized,22.29708324591197,0.9035097466470514,23.200592992559024
+CID0_SID1_PID290_MGID5_CAT116-CAT225-CAT3105,RandomWalkWithDrift,49.28000000000001,-11.885714285714316,61.16571428571432
+CID0_SID1_PID291_MGID5_CAT116-CAT227-CAT398,chronos2,34.63008989606585,16.35544150216239,50.98553139822823
+CID0_SID1_PID292_MGID5_CAT116-CAT227-CAT3104,WindowAverage,53.57142857142857,13.571428571428571,67.14285714285714
+CID0_SID1_PID293_MGID5_CAT116-CAT227-CAT3100,HistoricAverage,17.571428571428573,1.7857142857142858,19.357142857142858
+CID0_SID1_PID295_MGID5_CAT116-CAT225-CAT3106,HoltWinters,20.86647137165124,-3.103933450019005,23.970404821670243
+CID0_SID1_PID296_MGID5_CAT116-CAT225-CAT3103,CrostonSBA,43.25489734471574,-0.3585757298469372,43.61347307456268
+CID0_SID1_PID300_MGID6_CAT120-CAT250-CAT324,HoltWinters,284.13809817819833,-8.717823427076626,292.85592160527494
+CID0_SID1_PID304_MGID2_CAT131-CAT279-CAT3227,HistoricAverage,12.556390977443607,-0.6766917293233112,13.23308270676692
+CID0_SID1_PID321_MGID5_CAT118-CAT280-CAT3109,WindowAverage,21.0,-1.5714285714285714,22.571428571428573
+CID0_SID1_PID345_MGID5_CAT116-CAT226-CAT396,HoltWinters,13.492038835841036,-2.991938701762319,16.483977537603355
+CID0_SID1_PID362_MGID1_CAT17-CAT217-CAT351,lightgbm,45.77494616873181,-31.85421679835535,77.62916296708715
+CID0_SID1_PID366_MGID5_CAT116-CAT225-CAT3103,lightgbm,18.12391862070587,2.237053485271141,20.36097210597701
+CID0_SID1_PID368_MGID5_CAT118-CAT280-CAT3112,WindowAverage,15.0,-1.0714285714285714,16.071428571428573
+CID0_SID1_PID370_MGID6_CAT124-CAT266-CAT3199,RandomWalkWithDrift,41.21904761904762,-5.142857142857143,46.36190476190477
+CID0_SID1_PID373_MGID2_CAT131-CAT279-CAT322,HistoricAverage,27.96992481203008,-4.680451127819546,32.650375939849624
+CID0_SID1_PID374_MGID5_CAT118-CAT245-CAT393,HoltWinters,9.756191492005431,-1.269861789509806,11.026053281515235
+CID0_SID1_PID379_MGID2_CAT129-CAT276-CAT3231,RandomWalkWithDrift,19.65714285714285,-4.0,23.65714285714285
+CID0_SID1_PID381_MGID0_CAT128-CAT23-CAT37,SimpleExponentialSmoothingOptimized,49.02600817910298,36.33122859914452,85.3572367782475
+CID0_SID1_PID38_MGID0_CAT15-CAT26-CAT365,chronos2,72.80022185189384,-35.320929118565154,108.12115097045898
+CID0_SID1_PID411_MGID0_CAT128-CAT272-CAT3218,Naive,40.0,-1.4285714285714286,41.42857142857143
+CID0_SID1_PID415_MGID1_CAT17-CAT217-CAT342,lightgbm,24.110243843909554,7.995375985517865,32.10561982942742
+CID0_SID1_PID422_MGID5_CAT116-CAT225-CAT3101,CrostonSBA,33.79631808355545,2.215684721015504,36.012002804570955
+CID0_SID1_PID424_MGID5_CAT116-CAT227-CAT397,CrostonClassic,11.640182444207117,-0.0527056808783774,11.692888125085494
+CID0_SID1_PID439_MGID6_CAT121-CAT261-CAT316,Holt,40.33501145231434,-1.903099598638293,42.23811105095263
+CID0_SID1_PID452_MGID2_CAT129-CAT276-CAT360,WindowAverage,36.785714285714285,1.7857142857142858,38.57142857142857
+CID0_SID1_PID470_MGID5_CAT116-CAT225-CAT3101,OptimizedTheta,11.072042363270995,0.6200975055337258,11.692139868804723
+CID0_SID1_PID486_MGID6_CAT120-CAT268-CAT363,CrostonSBA,72.44933836854185,-0.0025114369358029,72.45184980547765
+CID0_SID1_PID487_MGID3_CAT111-CAT262-CAT3182,lightgbm,45.25908503889261,-42.84714962859767,88.10623466749028
+CID0_SID1_PID489_MGID6_CAT121-CAT264-CAT3123,HistoricAverage,55.78947368421053,-4.191729323308266,59.98120300751879
+CID0_SID1_PID496_MGID5_CAT116-CAT225-CAT3101,DynamicTheta,19.685056831210805,-0.0821760643207412,19.767232895531546
+CID0_SID1_PID499_MGID6_CAT120-CAT268-CAT318,HoltWinters,13.689659798887476,-2.032124719128676,15.721784518016152
+CID0_SID1_PID4_MGID2_CAT129-CAT278-CAT382,Holt,99.57007542029,-19.542798628578687,119.11287404886868
+CID0_SID1_PID500_MGID6_CAT14-CAT228-CAT3179,HoltWinters,22.57947241619524,1.1354172050901212,23.714889621285355
+CID0_SID1_PID554_MGID6_CAT14-CAT228-CAT3168,HoltWinters,47.28102786341327,-0.4042071468928847,47.68523501030616
+CID0_SID1_PID556_MGID5_CAT116-CAT225-CAT394,CrostonSBA,13.886182877610583,6.210617190615172,20.09680006822576
+CID0_SID1_PID563_MGID5_CAT116-CAT225-CAT3105,HistoricAverage,15.571428571428571,-4.035714285714286,19.607142857142858
+CID0_SID1_PID578_MGID6_CAT14-CAT253-CAT358,RandomWalkWithDrift,28.057142857142857,-9.714285714285715,37.77142857142857
+CID0_SID1_PID58_MGID5_CAT115-CAT242-CAT389,HistoricAverage,27.94924812030075,17.73872180451128,45.68796992481203
+CID0_SID1_PID592_MGID5_CAT115-CAT243-CAT311,HistoricAverage,22.18045112781955,-4.154135338345869,26.33458646616542
+CID0_SID1_PID596_MGID2_CAT129-CAT276-CAT360,chronos2,53.45385524204799,-3.3035169328962053,56.7573721749442
+CID0_SID1_PID60_MGID6_CAT121-CAT261-CAT3224,HoltWinters,19.77765357177695,-0.0562527127632935,19.83390628454024
+CID0_SID1_PID622_MGID6_CAT121-CAT261-CAT3178,CrostonSBA,27.86979446453844,-1.7414336598297413,29.611228124368186
+CID0_SID1_PID627_MGID4_CAT113-CAT21-CAT3214,HistoricAverage,19.285714285714285,4.285714285714286,23.57142857142857
+CID0_SID1_PID628_MGID6_CAT121-CAT261-CAT316,lightgbm,50.30055872996915,-35.04358082387715,85.3441395538463
+CID0_SID1_PID62_MGID0_CAT128-CAT252-CAT361,HoltWinters,32.78034452477277,-14.056822342107996,46.83716686688077
+CID0_SID1_PID631_MGID5_CAT116-CAT225-CAT3103,HistoricAverage,26.101503759398497,7.28947368421052,33.39097744360902
+CID0_SID1_PID633_MGID5_CAT116-CAT225-CAT3105,SeasonalExponentialSmoothingOptimized,12.140867125287892,-0.297030822011228,12.43789794729912
+CID0_SID1_PID634_MGID5_CAT116-CAT225-CAT399,Holt,14.609573874210977,0.6840621903794967,15.293636064590473
+CID0_SID1_PID635_MGID5_CAT116-CAT227-CAT3104,HoltWinters,19.9106020843305,1.0632351605172576,20.97383724484776
+CID0_SID1_PID636_MGID5_CAT116-CAT225-CAT3106,lightgbm,12.290998145667109,-0.0231351260622693,12.314133271729377
+CID0_SID1_PID638_MGID5_CAT116-CAT225-CAT394,SeasonalNaive,20.714285714285715,-0.7142857142857143,21.42857142857143
+CID0_SID1_PID63_MGID0_CAT128-CAT252-CAT3169,WindowAverage,40.71428571428572,-22.857142857142858,63.57142857142857
+CID0_SID1_PID644_MGID4_CAT123-CAT210-CAT317,DynamicTheta,26.483036243352338,2.979733634862697,29.462769878215035
+CID0_SID1_PID653_MGID1_CAT17-CAT217-CAT354,RandomWalkWithDrift,19.16,-10.342857142857136,29.50285714285713
+CID0_SID1_PID663_MGID6_CAT110-CAT233-CAT3186,chronos2,10.398132732936316,-0.0156463895525251,10.41377912248884
+CID0_SID1_PID670_MGID2_CAT131-CAT279-CAT3227,HoltWinters,24.35920022026841,-13.521317509080728,37.88051772934914
+CID0_SID1_PID686_MGID2_CAT10-CAT221-CAT3221,SeasonalExponentialSmoothingOptimized,29.895091740329573,-0.7899402779373149,30.685032018266888
+CID0_SID1_PID691_MGID6_CAT121-CAT264-CAT319,WindowAverage,242.8571428571429,-82.85714285714286,325.7142857142857
+CID0_SID1_PID6_MGID6_CAT120-CAT250-CAT3170,chronos2,53.31753540039063,-2.617542811802457,55.93507821219308
+CID0_SID1_PID706_MGID4_CAT127-CAT20-CAT30,WindowAverage,4.285714285714286,0.0,4.285714285714286
+CID0_SID1_PID70_MGID6_CAT14-CAT228-CAT381,OptimizedTheta,148.40973642672944,3.646377079635662,152.0561135063651
+CID0_SID1_PID711_MGID4_CAT127-CAT237-CAT3126,HistoricAverage,17.857142857142858,3.026315789473685,20.883458646616543
+CID0_SID1_PID717_MGID5_CAT116-CAT225-CAT3103,SimpleExponentialSmoothingOptimized,17.894526230043702,-0.4526021039798057,18.34712833402351
+CID0_SID1_PID719_MGID6_CAT110-CAT257-CAT3205,WindowAverage,16.785714285714285,1.0714285714285714,17.857142857142858
+CID0_SID1_PID738_MGID6_CAT14-CAT228-CAT381,RandomWalkWithDrift,18.23809523809524,-3.571428571428572,21.80952380952381
+CID0_SID1_PID740_MGID2_CAT129-CAT276-CAT360,SeasonalExponentialSmoothingOptimized,41.348114152310856,-3.660118279010893,45.00823243132175
+CID0_SID1_PID74_MGID6_CAT121-CAT261-CAT316,HoltWinters,8.666673939415025,2.9807652078785885,11.647439147293614
+CID0_SID1_PID764_MGID5_CAT118-CAT280-CAT3111,WindowAverage,22.5,-0.3571428571428571,22.857142857142858
+CID0_SID1_PID765_MGID5_CAT118-CAT280-CAT3112,lightgbm,16.665370963510902,0.3448704015189468,17.01024136502985
+CID0_SID1_PID768_MGID0_CAT15-CAT25-CAT36,SeasonalExponentialSmoothingOptimized,46.04064319155067,-4.336266775570465,50.376909967121136
+CID0_SID1_PID769_MGID6_CAT120-CAT268-CAT3176,HoltWinters,32.018586880418525,10.34559156192917,42.36417844234769
+CID0_SID1_PID76_MGID6_CAT18-CAT229-CAT3113,RandomWalkWithDrift,71.0,0.7142857142857143,71.71428571428571
+CID0_SID1_PID775_MGID6_CAT14-CAT228-CAT3167,Theta,59.26555151333589,0.3312839890385776,59.59683550237447
+CID0_SID1_PID783_MGID5_CAT122-CAT281-CAT356,OptimizedTheta,42.57097952290589,-6.277992408614874,48.848971931520765
+CID0_SID1_PID793_MGID3_CAT114-CAT240-CAT3146,chronos2,39.77202933175223,-5.101170131138393,44.87319946289063
+CID0_SID1_PID796_MGID6_CAT14-CAT228-CAT31,DynamicTheta,111.35465409014698,1.712452715148698,113.06710680529568
+CID0_SID1_PID79_MGID6_CAT14-CAT228-CAT310,lightgbm,10.752592875471018,-1.1841096804773394,11.936702555948358
+CID0_SID1_PID7_MGID2_CAT131-CAT279-CAT3228,HistoricAverage,11.428571428571429,0.9962406015037584,12.424812030075188
+CID0_SID1_PID806_MGID6_CAT121-CAT264-CAT3184,WindowAverage,24.285714285714285,-2.857142857142857,27.142857142857142
+CID0_SID1_PID810_MGID6_CAT14-CAT228-CAT3168,WindowAverage,47.142857142857146,2.142857142857143,49.28571428571429
+CID0_SID1_PID816_MGID4_CAT127-CAT20-CAT30,CrostonClassic,10.537066380827644,-1.6039879685450231,12.141054349372666
+CID0_SID1_PID822_MGID6_CAT120-CAT250-CAT3183,CrostonSBA,30.67594019651509,0.4458670898913287,31.12180728640642
+CID0_SID1_PID829_MGID3_CAT111-CAT235-CAT371,chronos2,13.020542825971331,-3.832501956394741,16.853044782366073
+CID0_SID1_PID830_MGID1_CAT17-CAT216-CAT344,CrostonClassic,26.42857142857144,-4.625413755209165,31.0539851837806
+CID0_SID1_PID834_MGID0_CAT128-CAT272-CAT3154,RandomWalkWithDrift,116.95238095238096,-76.2857142857143,193.23809523809527
+CID0_SID1_PID841_MGID2_CAT10-CAT213-CAT327,Naive,12.857142857142858,1.4285714285714286,14.285714285714286
+CID0_SID1_PID843_MGID6_CAT14-CAT228-CAT3167,SeasonalExponentialSmoothingOptimized,26.08135381504448,-2.46783920599582,28.5491930210403
+CID0_SID1_PID847_MGID6_CAT120-CAT250-CAT3229,Holt,15.072163553601351,-3.004629665819178,18.07679321942053
+CID0_SID1_PID858_MGID5_CAT115-CAT243-CAT311,Naive,10.714285714285714,-2.142857142857143,12.857142857142856
+CID0_SID1_PID90_MGID6_CAT14-CAT253-CAT377,WindowAverage,113.57142857142856,-15.357142857142858,128.92857142857142
+CID0_SID1_PID94_MGID6_CAT14-CAT228-CAT3216,WindowAverage,30.0,-11.428571428571429,41.42857142857143
+CID0_SID1_PID99_MGID6_CAT18-CAT229-CAT3115,WindowAverage,33.57142857142857,-13.214285714285714,46.785714285714285
+CID0_SID1_PID9_MGID2_CAT131-CAT279-CAT3232,SimpleExponentialSmoothingOptimized,19.427493183539266,-2.6390832852445567,22.06657646878382
+CID0_SID2_PID104_MGID6_CAT120-CAT250-CAT32,HoltWinters,54.63820730480289,0.8647721476746304,55.50297945247752
+CID0_SID2_PID115_MGID6_CAT110-CAT233-CAT3181,SeasonalExponentialSmoothingOptimized,19.4752965787653,-4.619005853392285,24.094302432157583
+CID0_SID2_PID118_MGID6_CAT14-CAT228-CAT3180,SeasonalExponentialSmoothingOptimized,27.79538420794287,0.430361826140178,28.22574603408305
+CID0_SID2_PID11_MGID5_CAT118-CAT280-CAT386,DynamicOptimizedTheta,12.142857142857142,-0.0588115745824698,12.201668717439611
+CID0_SID2_PID122_MGID6_CAT120-CAT268-CAT3127,OptimizedTheta,82.41259288344945,-61.95276487452424,144.3653577579737
+CID0_SID2_PID127_MGID6_CAT120-CAT258-CAT3172,chronos2,26.71504810878209,-0.0697299412318638,26.78477805001395
+CID0_SID2_PID129_MGID6_CAT110-CAT233-CAT3181,SeasonalExponentialSmoothingOptimized,34.87654915704686,-1.808011031766132,36.684560188813
+CID0_SID2_PID12_MGID5_CAT118-CAT280-CAT386,WindowAverage,13.928571428571429,-6.071428571428571,20.0
+CID0_SID2_PID135_MGID6_CAT14-CAT228-CAT3167,Theta,13.50357142857143,0.0424733183610785,13.546044746932507
+CID0_SID2_PID136_MGID6_CAT14-CAT228-CAT3161,WindowAverage,8.571428571428571,-1.4285714285714286,10.0
+CID0_SID2_PID140_MGID6_CAT14-CAT228-CAT310,Holt,19.07533532715331,-0.7531145799651462,19.828449907118458
+CID0_SID2_PID145_MGID5_CAT118-CAT280-CAT3110,CrostonSBA,11.26037851940758,-0.2315323249306123,11.491910844338191
+CID0_SID2_PID150_MGID6_CAT18-CAT229-CAT3116,SeasonalExponentialSmoothingOptimized,26.55319553651263,-16.47523342757108,43.02842896408371
+CID0_SID2_PID16_MGID6_CAT18-CAT229-CAT3113,Holt,11.254634692163489,3.7738595658595,15.028494258022986
+CID0_SID2_PID181_MGID5_CAT118-CAT280-CAT3112,SimpleExponentialSmoothingOptimized,22.3117987648,0.4683056393143029,22.7801044041143
+CID0_SID2_PID193_MGID6_CAT18-CAT229-CAT3114,CrostonSBA,23.57452155247673,-1.0606031377600138,24.635124690236747
+CID0_SID2_PID194_MGID5_CAT118-CAT280-CAT3109,Holt,35.469611585882284,-0.8410324581480749,36.31064404403036
+CID0_SID2_PID19_MGID6_CAT14-CAT228-CAT381,HistoricAverage,41.8421052631579,-2.124060150375941,43.96616541353384
+CID0_SID2_PID200_MGID6_CAT18-CAT229-CAT3113,HistoricAverage,16.842105263157894,-1.3909774436090256,18.23308270676692
+CID0_SID2_PID201_MGID6_CAT18-CAT229-CAT3113,OptimizedTheta,23.68084860158425,0.2982575477132556,23.979106149297504
+CID0_SID2_PID207_MGID6_CAT14-CAT228-CAT3179,HoltWinters,37.40488270048122,1.4252022126610078,38.83008491314222
+CID0_SID2_PID212_MGID6_CAT120-CAT250-CAT3229,OptimizedTheta,21.519799349292665,0.4724272650995585,21.992226614392223
+CID0_SID2_PID213_MGID6_CAT14-CAT228-CAT381,DynamicOptimizedTheta,15.010182782094304,0.0119030980743468,15.022085880168651
+CID0_SID2_PID215_MGID6_CAT14-CAT228-CAT3149,SimpleExponentialSmoothingOptimized,49.556278047661486,-1.677482237798147,51.23376028545963
+CID0_SID2_PID216_MGID6_CAT121-CAT264-CAT3123,CrostonSBA,27.857142857142858,0.4875910203018817,28.34473387744474
+CID0_SID2_PID21_MGID6_CAT14-CAT228-CAT381,CrostonClassic,28.391283706826723,1.7019355975350428,30.093219304361767
+CID0_SID2_PID220_MGID6_CAT18-CAT229-CAT3113,Holt,9.999781889175608,-1.0937263417592848,11.093508230934892
+CID0_SID2_PID223_MGID6_CAT120-CAT258-CAT3172,HistoricAverage,31.80451127819549,7.368421052631589,39.17293233082708
+CID0_SID2_PID23_MGID6_CAT14-CAT253-CAT358,RandomWalkWithDrift,36.285714285714285,-19.71428571428572,56.0
+CID0_SID2_PID240_MGID6_CAT14-CAT228-CAT3180,Holt,52.61575447493538,-34.65429072507295,87.27004520000833
+CID0_SID2_PID250_MGID5_CAT116-CAT227-CAT397,RandomWalkWithDrift,14.333333333333334,-0.7142857142857137,15.047619047619047
+CID0_SID2_PID26_MGID6_CAT14-CAT253-CAT3156,CrostonClassic,17.349536384777,-2.1519487752909305,19.50148516006793
+CID0_SID2_PID27_MGID6_CAT14-CAT228-CAT310,DynamicOptimizedTheta,10.771452085017968,-1.114450309411487,11.885902394429454
+CID0_SID2_PID290_MGID5_CAT116-CAT225-CAT3105,Naive,36.71428571428572,-0.1428571428571428,36.85714285714286
+CID0_SID2_PID291_MGID5_CAT116-CAT227-CAT398,HoltWinters,21.71842462805137,1.4507702310575417,23.16919485910892
+CID0_SID2_PID292_MGID5_CAT116-CAT227-CAT3104,chronos2,35.43684714181082,27.313066755022323,62.74991389683315
+CID0_SID2_PID300_MGID6_CAT120-CAT250-CAT324,RandomWalkWithDrift,148.17142857142855,-45.14285714285715,193.3142857142857
+CID0_SID2_PID321_MGID5_CAT118-CAT280-CAT3109,HistoricAverage,15.714285714285714,2.5357142857142856,18.25
+CID0_SID2_PID345_MGID5_CAT116-CAT226-CAT396,SeasonalExponentialSmoothingOptimized,17.287960563484358,-0.3600267209296827,17.64798728441404
+CID0_SID2_PID363_MGID6_CAT14-CAT253-CAT377,CrostonSBA,35.30757760323721,4.647950182758821,39.95552778599603
+CID0_SID2_PID379_MGID2_CAT129-CAT276-CAT3231,SeasonalNaive,25.714285714285715,5.714285714285714,31.42857142857143
+CID0_SID2_PID41_MGID6_CAT14-CAT253-CAT377,HistoricAverage,48.796992481203006,-34.14473684210526,82.94172932330827
+CID0_SID2_PID422_MGID5_CAT116-CAT225-CAT3101,WindowAverage,21.071428571428573,-1.0714285714285714,22.142857142857142
+CID0_SID2_PID452_MGID2_CAT129-CAT276-CAT360,WindowAverage,20.714285714285715,-0.3571428571428571,21.071428571428573
+CID0_SID2_PID473_MGID1_CAT17-CAT217-CAT346,HoltWinters,14.93687337676925,2.5308285490074303,17.46770192577668
+CID0_SID2_PID486_MGID6_CAT120-CAT268-CAT363,CrostonSBA,35.6224176097496,27.89274734840929,63.51516495815889
+CID0_SID2_PID496_MGID5_CAT116-CAT225-CAT3101,Holt,19.795832132959585,-5.562539923050094,25.35837205600968
+CID0_SID2_PID4_MGID2_CAT129-CAT278-CAT382,Naive,83.57142857142857,0.7142857142857143,84.28571428571428
+CID0_SID2_PID500_MGID6_CAT14-CAT228-CAT3179,chronos2,14.71936525617327,-1.6552848815917969,16.374650137765066
+CID0_SID2_PID549_MGID2_CAT131-CAT279-CAT3230,CrostonClassic,32.82539421726811,0.4682630967044576,33.29365731397257
+CID0_SID2_PID554_MGID6_CAT14-CAT228-CAT3168,Naive,27.857142857142858,3.571428571428572,31.42857142857143
+CID0_SID2_PID556_MGID5_CAT116-CAT225-CAT394,OptimizedTheta,12.2799582954029,2.912335509727456,15.192293805130356
+CID0_SID2_PID567_MGID6_CAT120-CAT250-CAT359,HoltWinters,19.024203816244192,-0.966220178331302,19.99042399457549
+CID0_SID2_PID578_MGID6_CAT14-CAT253-CAT358,CrostonSBA,27.50785376738525,1.7307379140175894,29.23859168140284
+CID0_SID2_PID580_MGID2_CAT129-CAT276-CAT360,DynamicOptimizedTheta,73.75209396180672,10.989471723466377,84.7415656852731
+CID0_SID2_PID58_MGID5_CAT115-CAT242-CAT389,CrostonSBA,18.20046337231062,-0.04837819691285,18.24884156922347
+CID0_SID2_PID600_MGID2_CAT129-CAT278-CAT3157,WindowAverage,77.14285714285714,-65.71428571428571,142.85714285714283
+CID0_SID2_PID633_MGID5_CAT116-CAT225-CAT3105,CrostonSBA,12.98749193726111,8.399385948847348,21.38687788610845
+CID0_SID2_PID634_MGID5_CAT116-CAT225-CAT399,WindowAverage,8.214285714285714,-3.928571428571429,12.142857142857142
+CID0_SID2_PID635_MGID5_CAT116-CAT227-CAT3104,WindowAverage,15.0,0.0,15.0
+CID0_SID2_PID638_MGID5_CAT116-CAT225-CAT394,Holt,18.36576576022726,2.2808146193880314,20.64658037961529
+CID0_SID2_PID644_MGID4_CAT123-CAT210-CAT317,CrostonClassic,24.088308774036697,0.380509280557009,24.468818054593708
+CID0_SID2_PID645_MGID6_CAT124-CAT269-CAT3139,WindowAverage,42.0,-8.0,50.0
+CID0_SID2_PID650_MGID6_CAT110-CAT233-CAT3160,CrostonClassic,21.428571428571423,0.0934907079329333,21.52206213650436
+CID0_SID2_PID670_MGID2_CAT131-CAT279-CAT3227,HoltWinters,20.128835273316124,0.635264549762954,20.76409982307908
+CID0_SID2_PID686_MGID2_CAT10-CAT221-CAT3221,chronos2,11.988862446376256,1.949472972324916,13.938335418701172
+CID0_SID2_PID691_MGID6_CAT121-CAT264-CAT319,WindowAverage,120.0,-12.857142857142858,132.85714285714286
+CID0_SID2_PID6_MGID6_CAT120-CAT250-CAT3170,HoltWinters,36.86519969485119,-1.7428289988349153,38.6080286936861
+CID0_SID2_PID70_MGID6_CAT14-CAT228-CAT381,chronos2,85.5773446219308,-5.505000523158482,91.08234514508928
+CID0_SID2_PID715_MGID6_CAT110-CAT238-CAT3190,CrostonClassic,34.68060358084632,-2.189316038466447,36.86991961931277
+CID0_SID2_PID719_MGID6_CAT110-CAT257-CAT3205,WindowAverage,17.357142857142858,0.2142857142857142,17.571428571428573
+CID0_SID2_PID728_MGID6_CAT120-CAT258-CAT374,Holt,21.46753176368835,-5.435068041454154,26.902599805142504
+CID0_SID2_PID76_MGID6_CAT18-CAT229-CAT3113,Holt,43.54310996780045,-3.3986330273782235,46.94174299517867
+CID0_SID2_PID774_MGID6_CAT14-CAT253-CAT377,Naive,36.42857142857143,3.571428571428572,40.0
+CID0_SID2_PID775_MGID6_CAT14-CAT228-CAT3167,HistoricAverage,64.4360902255639,2.4812030075187983,66.9172932330827
+CID0_SID2_PID77_MGID6_CAT18-CAT229-CAT3113,chronos2,24.279197147914346,0.2114731924874442,24.490670340401785
+CID0_SID2_PID783_MGID5_CAT122-CAT281-CAT356,WindowAverage,38.92857142857143,0.3571428571428571,39.285714285714285
+CID0_SID2_PID802_MGID0_CAT15-CAT27-CAT366,HistoricAverage,66.74812030075189,-12.951127819548883,79.69924812030078
+CID0_SID2_PID829_MGID3_CAT111-CAT235-CAT371,Naive,21.428571428571427,8.571428571428571,30.0
+CID0_SID2_PID834_MGID0_CAT128-CAT272-CAT3154,Holt,54.711867319381966,-37.956205148918585,92.66807246830056
+CID0_SID2_PID843_MGID6_CAT14-CAT228-CAT3167,lightgbm,16.72842873599509,-2.229648804294808,18.9580775402899
+CID0_SID2_PID93_MGID6_CAT121-CAT261-CAT3223,Theta,16.598306805975977,-0.042550410171021,16.640857216146998
+CID0_SID8_PID411_MGID0_CAT128-CAT272-CAT3218,Holt,39.34180909139396,-0.4776973866143724,39.819506478008336
+CID0_SID8_PID90_MGID6_CAT14-CAT253-CAT377,SeasonalExponentialSmoothingOptimized,45.79869826459655,12.41364429640701,58.21234256100355

metrics/chronos_metrics.csv

@@ -0,0 +1,298 @@
+id,model,mae,bias,score
+CID0_SID0_PID104_MGID6_CAT120-CAT250-CAT32,chronos2,80.6895991734096,-80.6399427141462,161.32954188755582
+CID0_SID0_PID117_MGID6_CAT14-CAT228-CAT31,chronos2,99.66533551897321,-45.48818097795759,145.1535164969308
+CID0_SID0_PID118_MGID6_CAT14-CAT228-CAT3180,chronos2,52.22617667061942,-43.521396092006135,95.74757276262555
+CID0_SID0_PID122_MGID6_CAT120-CAT268-CAT3127,chronos2,98.14736611502511,-81.40784890311105,179.55521501813615
+CID0_SID0_PID127_MGID6_CAT120-CAT258-CAT3172,chronos2,21.079993111746653,-15.380333491734095,36.46032660348075
+CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181,chronos2,50.955439703805105,-29.823019572666713,80.77845927647182
+CID0_SID0_PID136_MGID6_CAT14-CAT228-CAT3161,chronos2,27.290582384381974,-21.575023651123047,48.865606035505024
+CID0_SID0_PID166_MGID6_CAT120-CAT250-CAT359,chronos2,74.86003984723773,54.94857134137835,129.80861118861608
+CID0_SID0_PID18_MGID6_CAT14-CAT228-CAT3142,chronos2,40.3356876373291,-33.11205809456961,73.44774573189872
+CID0_SID0_PID190_MGID6_CAT14-CAT228-CAT3131,chronos2,86.4818594796317,-53.943292890276226,140.42515236990792
+CID0_SID0_PID193_MGID6_CAT18-CAT229-CAT3114,chronos2,15.405316489083427,-1.767239706856864,17.17255619594029
+CID0_SID0_PID194_MGID5_CAT118-CAT280-CAT3109,chronos2,36.03912244524275,-27.062531062534877,63.10165350777763
+CID0_SID0_PID19_MGID6_CAT14-CAT228-CAT381,chronos2,34.48450034005301,-3.917047773088728,38.401548113141736
+CID0_SID0_PID201_MGID6_CAT18-CAT229-CAT3113,chronos2,33.04564830235073,3.284590039934431,36.330238342285156
+CID0_SID0_PID207_MGID6_CAT14-CAT228-CAT3179,chronos2,30.17083249773298,-8.355783190046038,38.52661568777902
+CID0_SID0_PID214_MGID6_CAT18-CAT28-CAT39,chronos2,14.461611883980888,7.242711475917271,21.704323359898158
+CID0_SID0_PID215_MGID6_CAT14-CAT228-CAT3149,chronos2,75.59569658551898,-41.36132158551897,116.95701817103796
+CID0_SID0_PID21_MGID6_CAT14-CAT228-CAT381,chronos2,26.31028311593192,1.7284011840820312,28.03868430001395
+CID0_SID0_PID223_MGID6_CAT120-CAT258-CAT3172,chronos2,28.58562469482422,-10.282992771693639,38.86861746651786
+CID0_SID0_PID23_MGID6_CAT14-CAT253-CAT358,chronos2,65.36184528895787,-57.724832262311665,123.08667755126953
+CID0_SID0_PID253_MGID6_CAT110-CAT233-CAT383,chronos2,16.563588278634207,9.696412222726005,26.26000050136021
+CID0_SID0_PID259_MGID6_CAT121-CAT264-CAT3123,chronos2,32.164373125348774,24.3430666242327,56.50743974958148
+CID0_SID0_PID26_MGID6_CAT14-CAT253-CAT3156,chronos2,29.082967485700333,-5.741135733468192,34.82410321916853
+CID0_SID0_PID291_MGID5_CAT116-CAT227-CAT398,chronos2,25.01334544590541,11.07628413609096,36.08962958199637
+CID0_SID0_PID296_MGID5_CAT116-CAT225-CAT3103,chronos2,30.991492135184153,7.773103986467634,38.764596121651785
+CID0_SID0_PID300_MGID6_CAT120-CAT250-CAT324,chronos2,175.685546875,15.197344098772321,190.88289097377233
+CID0_SID0_PID310_MGID5_CAT116-CAT225-CAT3105,chronos2,40.98636518205915,-5.044943673270089,46.03130885532924
+CID0_SID0_PID345_MGID5_CAT116-CAT226-CAT396,chronos2,11.358805656433105,-5.655761310032436,17.01456696646554
+CID0_SID0_PID370_MGID6_CAT124-CAT266-CAT3199,chronos2,30.16170528956822,-25.142948423113143,55.304653712681365
+CID0_SID0_PID379_MGID2_CAT129-CAT276-CAT3231,chronos2,25.415206909179688,17.135162353515625,42.55036926269531
+CID0_SID0_PID38_MGID0_CAT15-CAT26-CAT365,chronos2,40.657314845493865,-26.475226266043528,67.13254111153739
+CID0_SID0_PID411_MGID0_CAT128-CAT272-CAT3218,chronos2,36.636534690856934,-30.28224958692278,66.91878427777971
+CID0_SID0_PID419_MGID6_CAT124-CAT251-CAT3153,chronos2,18.35092272077288,-3.584425789969308,21.935348510742188
+CID0_SID0_PID41_MGID6_CAT14-CAT253-CAT377,chronos2,62.22787094116211,-15.99260003226144,78.22047097342355
+CID0_SID0_PID439_MGID6_CAT121-CAT261-CAT316,chronos2,36.892307553972515,-31.434713091169083,68.3270206451416
+CID0_SID0_PID486_MGID6_CAT120-CAT268-CAT363,chronos2,70.30577087402344,64.00670732770648,134.3124782017299
+CID0_SID0_PID489_MGID6_CAT121-CAT264-CAT3123,chronos2,29.32832636151995,0.31784956795828684,29.646175929478236
+CID0_SID0_PID496_MGID5_CAT116-CAT225-CAT3101,chronos2,21.22608402797154,8.63675035749163,29.862834385463174
+CID0_SID0_PID4_MGID2_CAT129-CAT278-CAT382,chronos2,162.0698514665876,162.0698514665876,324.1397029331752
+CID0_SID0_PID500_MGID6_CAT14-CAT228-CAT3179,chronos2,17.155797140938894,-6.086963653564453,23.242760794503347
+CID0_SID0_PID548_MGID3_CAT111-CAT262-CAT3182,chronos2,17.2822208404541,-6.249020985194615,23.531241825648717
+CID0_SID0_PID554_MGID6_CAT14-CAT228-CAT3168,chronos2,36.327914646693635,-22.343670436314174,58.67158508300781
+CID0_SID0_PID567_MGID6_CAT120-CAT250-CAT359,chronos2,29.832672119140625,21.116050720214844,50.94872283935547
+CID0_SID0_PID578_MGID6_CAT14-CAT253-CAT358,chronos2,26.22887965611049,-16.940819876534597,43.16969953264508
+CID0_SID0_PID580_MGID2_CAT129-CAT276-CAT360,chronos2,38.82637241908482,-33.46745736258371,72.29382978166853
+CID0_SID0_PID596_MGID2_CAT129-CAT276-CAT360,chronos2,31.63235637119838,9.836424146379743,41.468780517578125
+CID0_SID0_PID600_MGID2_CAT129-CAT278-CAT3157,chronos2,69.66888155256,-69.66888155256,139.33776310512
+CID0_SID0_PID631_MGID5_CAT116-CAT225-CAT3103,chronos2,22.781345094953263,-0.7412499019077846,23.52259499686105
+CID0_SID0_PID633_MGID5_CAT116-CAT225-CAT3105,chronos2,16.704180308750697,-5.442446844918387,22.146627153669083
+CID0_SID0_PID635_MGID5_CAT116-CAT227-CAT3104,chronos2,15.947186606270927,-6.389308112008231,22.33649471827916
+CID0_SID0_PID638_MGID5_CAT116-CAT225-CAT394,chronos2,19.238462720598495,2.5955172947474887,21.833980015345983
+CID0_SID0_PID644_MGID4_CAT123-CAT210-CAT317,chronos2,29.257168906075613,29.257168906075613,58.514337812151226
+CID0_SID0_PID672_MGID6_CAT110-CAT238-CAT3173,chronos2,35.36400672367641,-19.447684832981654,54.81169155665806
+CID0_SID0_PID686_MGID2_CAT10-CAT221-CAT3221,chronos2,9.82526125226702,0.03381456647600446,9.859075818743026
+CID0_SID0_PID691_MGID6_CAT121-CAT264-CAT319,chronos2,143.0016370500837,-75.28514535086495,218.28678240094865
+CID0_SID0_PID699_MGID2_CAT130-CAT275-CAT3191,chronos2,30.683497837611608,13.609600612095424,44.29309844970703
+CID0_SID0_PID6_MGID6_CAT120-CAT250-CAT3170,chronos2,33.580575125558035,-18.545035226004465,52.1256103515625
+CID0_SID0_PID70_MGID6_CAT14-CAT228-CAT381,chronos2,113.48904418945312,36.706107003348215,150.19515119280135
+CID0_SID0_PID712_MGID6_CAT124-CAT266-CAT3199,chronos2,74.38934326171875,2.1574598039899553,76.5468030657087
+CID0_SID0_PID719_MGID6_CAT110-CAT257-CAT3205,chronos2,12.023933955601283,6.47154290335519,18.495476858956472
+CID0_SID0_PID72_MGID3_CAT125-CAT271-CAT3213,chronos2,17.24076243809291,0.4336934770856585,17.67445591517857
+CID0_SID0_PID740_MGID2_CAT129-CAT276-CAT360,chronos2,40.26706968035017,1.6595649719238281,41.926634652274
+CID0_SID0_PID764_MGID5_CAT118-CAT280-CAT3111,chronos2,18.208662578037806,2.884938648768834,21.09360122680664
+CID0_SID0_PID769_MGID6_CAT120-CAT268-CAT3176,chronos2,24.10213197980608,-3.3105081830705916,27.412640162876674
+CID0_SID0_PID76_MGID6_CAT18-CAT229-CAT3113,chronos2,35.644408634730745,1.6273645673479353,37.27177320207868
+CID0_SID0_PID775_MGID6_CAT14-CAT228-CAT3167,chronos2,34.24105725969587,-18.452657427106583,52.69371468680245
+CID0_SID0_PID783_MGID5_CAT122-CAT281-CAT356,chronos2,42.21648515973772,-31.490479605538503,73.70696476527623
+CID0_SID0_PID796_MGID6_CAT14-CAT228-CAT31,chronos2,58.79883411952427,-36.63224084036691,95.43107495989119
+CID0_SID0_PID806_MGID6_CAT121-CAT264-CAT3184,chronos2,22.50575746808733,-15.234773908342634,37.74053137642996
+CID0_SID0_PID810_MGID6_CAT14-CAT228-CAT3168,chronos2,53.15536608014788,-8.268820626395092,61.42418670654297
+CID0_SID0_PID834_MGID0_CAT128-CAT272-CAT3154,chronos2,147.4202504839216,-147.4202504839216,294.8405009678432
+CID0_SID0_PID843_MGID6_CAT14-CAT228-CAT3167,chronos2,22.585813522338867,-6.574656077793667,29.160469600132533
+CID0_SID0_PID90_MGID6_CAT14-CAT253-CAT377,chronos2,92.47123282296317,26.820539202008927,119.2917720249721
+CID0_SID0_PID93_MGID6_CAT121-CAT261-CAT3223,chronos2,25.791872024536133,-9.48187610081264,35.273748125348774
+CID0_SID12_PID129_MGID6_CAT110-CAT233-CAT3181,chronos2,25.36536639077323,-20.2901770727975,45.65554346357073
+CID0_SID12_PID41_MGID6_CAT14-CAT253-CAT377,chronos2,50.08167280469622,-50.08167280469622,100.16334560939244
+CID0_SID12_PID768_MGID0_CAT15-CAT25-CAT36,chronos2,66.01276424952916,-50.13180078778948,116.14456503731864
+CID0_SID18_PID362_MGID1_CAT17-CAT217-CAT351,chronos2,32.20820617675781,-14.391299656459264,46.59950583321708
+CID0_SID18_PID536_MGID0_CAT15-CAT26-CAT365,chronos2,109.1411988394601,-104.91946138654437,214.06066022600447
+CID0_SID18_PID830_MGID1_CAT17-CAT216-CAT344,chronos2,25.89458097730364,-15.26977607182094,41.16435704912458
+CID0_SID19_PID768_MGID0_CAT15-CAT25-CAT36,chronos2,36.29009396689279,-30.86663750239781,67.1567314692906
+CID0_SID1_PID104_MGID6_CAT120-CAT250-CAT32,chronos2,96.32394627162388,-48.27830941336496,144.60225568498885
+CID0_SID1_PID108_MGID6_CAT18-CAT229-CAT3113,chronos2,34.99216788155692,-11.183612278529576,46.17578016008649
+CID0_SID1_PID110_MGID2_CAT131-CAT279-CAT3121,chronos2,15.83150236947196,2.7161382947649275,18.547640664236887
+CID0_SID1_PID114_MGID6_CAT110-CAT233-CAT372,chronos2,107.98474884033203,-64.21888405936105,172.20363289969308
+CID0_SID1_PID117_MGID6_CAT14-CAT228-CAT31,chronos2,202.7659955705915,-67.96686226981028,270.7328578404018
+CID0_SID1_PID118_MGID6_CAT14-CAT228-CAT3180,chronos2,59.13787623814174,-43.30914088657924,102.44701712472099
+CID0_SID1_PID121_MGID2_CAT130-CAT274-CAT3195,chronos2,24.11404527936663,-12.24346651349749,36.357511792864116
+CID0_SID1_PID122_MGID6_CAT120-CAT268-CAT3127,chronos2,143.64701189313615,-131.9451686314174,275.59218052455356
+CID0_SID1_PID127_MGID6_CAT120-CAT258-CAT3172,chronos2,47.05058125087193,-8.503939492361885,55.554520743233816
+CID0_SID1_PID133_MGID2_CAT131-CAT277-CAT373,chronos2,34.16376767839704,-27.736861637660436,61.900629316057476
+CID0_SID1_PID136_MGID6_CAT14-CAT228-CAT3161,chronos2,23.632112775530135,-14.62834984915597,38.260462624686106
+CID0_SID1_PID138_MGID4_CAT127-CAT237-CAT3126,chronos2,25.68507330758231,12.409557342529297,38.09463065011161
+CID0_SID1_PID140_MGID6_CAT14-CAT228-CAT310,chronos2,16.790157863071986,-3.0432303292410716,19.83338819231306
+CID0_SID1_PID151_MGID2_CAT131-CAT279-CAT3158,chronos2,11.726647240774971,-0.03992434910365513,11.766571589878627
+CID0_SID1_PID166_MGID6_CAT120-CAT250-CAT359,chronos2,85.53926522391183,-2.8530338832310287,88.39229910714286
+CID0_SID1_PID16_MGID6_CAT18-CAT229-CAT3113,chronos2,31.12902777535575,0.9658083234514508,32.0948360988072
+CID0_SID1_PID17_MGID6_CAT120-CAT258-CAT3172,chronos2,16.201471873692103,11.559507914951869,27.760979788643972
+CID0_SID1_PID18_MGID6_CAT14-CAT228-CAT3142,chronos2,53.62057222638811,-44.779757908412385,98.4003301348005
+CID0_SID1_PID190_MGID6_CAT14-CAT228-CAT3131,chronos2,136.1039036342076,-107.84723881312779,243.95114244733537
+CID0_SID1_PID194_MGID5_CAT118-CAT280-CAT3109,chronos2,75.29881068638393,-57.77859279087612,133.07740347726005
+CID0_SID1_PID19_MGID6_CAT14-CAT228-CAT381,chronos2,76.30308859688895,-33.57009778703962,109.87318638392858
+CID0_SID1_PID200_MGID6_CAT18-CAT229-CAT3113,chronos2,18.47067642211914,-5.7105898175920755,24.181266239711217
+CID0_SID1_PID201_MGID6_CAT18-CAT229-CAT3113,chronos2,25.198870522635325,-16.72930199759347,41.9281725202288
+CID0_SID1_PID207_MGID6_CAT14-CAT228-CAT3179,chronos2,49.47850036621094,-13.578122820172991,63.05662318638393
+CID0_SID1_PID213_MGID6_CAT14-CAT228-CAT381,chronos2,15.143624986921038,-0.8733209882463727,16.01694597516741
+CID0_SID1_PID214_MGID6_CAT18-CAT28-CAT39,chronos2,20.225445883614675,-3.765418461390904,23.99086434500558
+CID0_SID1_PID215_MGID6_CAT14-CAT228-CAT3149,chronos2,130.78228759765625,-15.932730538504464,146.71501813616072
+CID0_SID1_PID216_MGID6_CAT121-CAT264-CAT3123,chronos2,17.586494990757533,-1.8066727774483817,19.393167768205913
+CID0_SID1_PID219_MGID6_CAT18-CAT229-CAT3114,chronos2,23.760510308401926,-1.5513428279331751,25.311853136335102
+CID0_SID1_PID220_MGID6_CAT18-CAT229-CAT3113,chronos2,27.720102037702286,1.4723281860351562,29.192430223737443
+CID0_SID1_PID23_MGID6_CAT14-CAT253-CAT358,chronos2,77.60948671613421,-62.295858655657085,139.9053453717913
+CID0_SID1_PID247_MGID5_CAT116-CAT225-CAT394,chronos2,34.05023520333426,-29.42029789515904,63.470533098493306
+CID0_SID1_PID249_MGID5_CAT116-CAT225-CAT3103,chronos2,17.3141416822161,12.332730157034737,29.646871839250835
+CID0_SID1_PID250_MGID5_CAT116-CAT227-CAT397,chronos2,18.99221692766462,-7.611866542271206,26.604083469935826
+CID0_SID1_PID258_MGID2_CAT131-CAT279-CAT3121,chronos2,21.477943965366908,-14.549320765904017,36.027264731270925
+CID0_SID1_PID259_MGID6_CAT121-CAT264-CAT3123,chronos2,42.083404541015625,30.09627205984933,72.17967660086495
+CID0_SID1_PID26_MGID6_CAT14-CAT253-CAT3156,chronos2,35.68497739519392,5.684563228062221,41.36954062325614
+CID0_SID1_PID27_MGID6_CAT14-CAT228-CAT310,chronos2,12.109777041843959,3.584414209638323,15.694191251482282
+CID0_SID1_PID285_MGID6_CAT124-CAT251-CAT3203,chronos2,27.42757769993373,1.228188923427037,28.65576662336077
+CID0_SID1_PID290_MGID5_CAT116-CAT225-CAT3105,chronos2,53.163927350725444,-19.907017299107142,73.07094464983258
+CID0_SID1_PID291_MGID5_CAT116-CAT227-CAT398,chronos2,34.63008989606585,16.35544150216239,50.98553139822823
+CID0_SID1_PID292_MGID5_CAT116-CAT227-CAT3104,chronos2,54.983921595982146,36.572400774274556,91.55632237025671
+CID0_SID1_PID293_MGID5_CAT116-CAT227-CAT3100,chronos2,22.233236857822963,-19.13541385105678,41.36865070887974
+CID0_SID1_PID295_MGID5_CAT116-CAT225-CAT3106,chronos2,19.979087012154714,-6.3613932473318915,26.340480259486604
+CID0_SID1_PID296_MGID5_CAT116-CAT225-CAT3103,chronos2,42.2214366367885,-5.03663090297154,47.25806753976004
+CID0_SID1_PID300_MGID6_CAT120-CAT250-CAT324,chronos2,361.40216064453125,-126.23518589564732,487.63734654017856
+CID0_SID1_PID304_MGID2_CAT131-CAT279-CAT3227,chronos2,12.314241681780134,-5.152464185442243,17.466705867222377
+CID0_SID1_PID321_MGID5_CAT118-CAT280-CAT3109,chronos2,17.858971459524973,-15.27560670035226,33.134578159877236
+CID0_SID1_PID345_MGID5_CAT116-CAT226-CAT396,chronos2,17.015483856201172,-4.999752044677734,22.015235900878906
+CID0_SID1_PID362_MGID1_CAT17-CAT217-CAT351,chronos2,66.99385125296456,-66.56591742379325,133.5597686767578
+CID0_SID1_PID366_MGID5_CAT116-CAT225-CAT3103,chronos2,19.574784960065568,-7.743248530796596,27.318033490862163
+CID0_SID1_PID368_MGID5_CAT118-CAT280-CAT3112,chronos2,14.179617200578962,6.295285906110491,20.474903106689453
+CID0_SID1_PID370_MGID6_CAT124-CAT266-CAT3199,chronos2,46.91345814296177,-35.29910332815988,82.21256147112166
+CID0_SID1_PID373_MGID2_CAT131-CAT279-CAT322,chronos2,29.69803019932338,20.949629102434432,50.64765930175781
+CID0_SID1_PID374_MGID5_CAT118-CAT245-CAT393,chronos2,9.490434646606445,-4.954645701817104,14.445080348423549
+CID0_SID1_PID379_MGID2_CAT129-CAT276-CAT3231,chronos2,44.47200448172433,44.47200448172433,88.94400896344867
+CID0_SID1_PID381_MGID0_CAT128-CAT23-CAT37,chronos2,59.42442321777344,51.83275713239397,111.2571803501674
+CID0_SID1_PID38_MGID0_CAT15-CAT26-CAT365,chronos2,72.80022185189384,-35.320929118565154,108.12115097045898
+CID0_SID1_PID411_MGID0_CAT128-CAT272-CAT3218,chronos2,35.87381853376116,-18.267080579485214,54.14089911324638
+CID0_SID1_PID415_MGID1_CAT17-CAT217-CAT342,chronos2,25.705340794154576,12.622191292898995,38.32753208705357
+CID0_SID1_PID422_MGID5_CAT116-CAT225-CAT3101,chronos2,33.77666636875698,3.6805817740304128,37.45724814278739
+CID0_SID1_PID424_MGID5_CAT116-CAT227-CAT397,chronos2,12.354221071515765,1.9355896541050501,14.289810725620814
+CID0_SID1_PID439_MGID6_CAT121-CAT261-CAT316,chronos2,64.8098258972168,59.980874742780415,124.79070063999721
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+CID0_SID1_PID470_MGID5_CAT116-CAT225-CAT3101,chronos2,10.701764106750488,1.6018528257097517,12.30361693246024
+CID0_SID1_PID486_MGID6_CAT120-CAT268-CAT363,chronos2,80.0389404296875,-6.458086286272324,86.49702671595982
+CID0_SID1_PID487_MGID3_CAT111-CAT262-CAT3182,chronos2,53.48441205705915,-52.936323983328684,106.42073604038784
+CID0_SID1_PID489_MGID6_CAT121-CAT264-CAT3123,chronos2,52.765547616141184,20.243186950683594,73.00873456682478
+CID0_SID1_PID496_MGID5_CAT116-CAT225-CAT3101,chronos2,22.463374546595983,-8.723808288574219,31.1871828351702
+CID0_SID1_PID499_MGID6_CAT120-CAT268-CAT318,chronos2,13.983515739440918,-6.329907144818987,20.313422884259907
+CID0_SID1_PID4_MGID2_CAT129-CAT278-CAT382,chronos2,198.5143781389509,198.5143781389509,397.0287562779018
+CID0_SID1_PID500_MGID6_CAT14-CAT228-CAT3179,chronos2,32.177310943603516,9.606177738734655,41.783488682338174
+CID0_SID1_PID554_MGID6_CAT14-CAT228-CAT3168,chronos2,56.82628304617746,1.8012804303850427,58.6275634765625
+CID0_SID1_PID556_MGID5_CAT116-CAT225-CAT394,chronos2,13.843205315726143,7.341894694737026,21.18510001046317
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+CID0_SID1_PID578_MGID6_CAT14-CAT253-CAT358,chronos2,36.181428636823384,-32.511866433279856,68.69329507010323
+CID0_SID1_PID58_MGID5_CAT115-CAT242-CAT389,chronos2,28.31017848423549,25.171732221330917,53.481910705566406
+CID0_SID1_PID592_MGID5_CAT115-CAT243-CAT311,chronos2,28.676744733537948,13.03885269165039,41.715597425188335
+CID0_SID1_PID596_MGID2_CAT129-CAT276-CAT360,chronos2,53.45385524204799,-3.3035169328962053,56.7573721749442
+CID0_SID1_PID60_MGID6_CAT121-CAT261-CAT3224,chronos2,20.799895422799246,-5.7807222093854636,26.58061763218471
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+CID0_SID1_PID627_MGID4_CAT113-CAT21-CAT3214,chronos2,27.89814349583217,-26.974416732788086,54.87256022862026
+CID0_SID1_PID628_MGID6_CAT121-CAT261-CAT316,chronos2,60.38991328648159,-56.019412994384766,116.40932628086635
+CID0_SID1_PID62_MGID0_CAT128-CAT252-CAT361,chronos2,38.385043552943635,-22.934334346226283,61.31937789916992
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+CID0_SID1_PID634_MGID5_CAT116-CAT225-CAT399,chronos2,18.700610842023575,-10.865692956107003,29.566303798130576
+CID0_SID1_PID635_MGID5_CAT116-CAT227-CAT3104,chronos2,25.989718300955637,-9.606622423444476,35.596340724400115
+CID0_SID1_PID636_MGID5_CAT116-CAT225-CAT3106,chronos2,10.145640781947545,-5.874548775809152,16.020189557756698
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+CID0_SID1_PID63_MGID0_CAT128-CAT252-CAT3169,chronos2,55.28023311070034,-50.839622497558594,106.11985560825893
+CID0_SID1_PID644_MGID4_CAT123-CAT210-CAT317,chronos2,32.08821541922433,-2.8753444126674106,34.96355983189174
+CID0_SID1_PID653_MGID1_CAT17-CAT217-CAT354,chronos2,28.625655582972936,-26.788758959089005,55.41441454206194
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+CID0_SID1_PID670_MGID2_CAT131-CAT279-CAT3227,chronos2,26.88415036882673,-17.283467973981583,44.16761834280831
+CID0_SID1_PID686_MGID2_CAT10-CAT221-CAT3221,chronos2,32.75663321358817,3.3807405744280135,36.13737378801618
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+CID0_SID1_PID706_MGID4_CAT127-CAT20-CAT30,chronos2,4.8192533765520364,-1.277853557041713,6.09710693359375
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+CID0_SID1_PID711_MGID4_CAT127-CAT237-CAT3126,chronos2,36.156241280691965,31.53070068359375,67.68694196428572
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+CID0_SID1_PID764_MGID5_CAT118-CAT280-CAT3111,chronos2,23.028662545340403,-5.897566659109933,28.926229204450337
+CID0_SID1_PID765_MGID5_CAT118-CAT280-CAT3112,chronos2,16.90654972621373,-2.7114622933523997,19.61801201956613
+CID0_SID1_PID768_MGID0_CAT15-CAT25-CAT36,chronos2,56.1641720363072,-29.454581124441965,85.61875316074916
+CID0_SID1_PID769_MGID6_CAT120-CAT268-CAT3176,chronos2,32.86247362409319,21.32725851876395,54.18973214285714
+CID0_SID1_PID76_MGID6_CAT18-CAT229-CAT3113,chronos2,71.9222172328404,-14.395952497209821,86.31816973005022
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+CID0_SID1_PID783_MGID5_CAT122-CAT281-CAT356,chronos2,43.30845315115793,10.924073355538505,54.23252650669643
+CID0_SID1_PID793_MGID3_CAT114-CAT240-CAT3146,chronos2,39.77202933175223,-5.101170131138393,44.873199462890625
+CID0_SID1_PID796_MGID6_CAT14-CAT228-CAT31,chronos2,110.45704214913505,-9.764258248465401,120.22130039760044
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+CID0_SID1_PID806_MGID6_CAT121-CAT264-CAT3184,chronos2,30.22834042140416,-25.180807658604213,55.40914808000837
+CID0_SID1_PID810_MGID6_CAT14-CAT228-CAT3168,chronos2,45.908412388392854,-19.054469517299108,64.96288190569196
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+CID0_SID1_PID834_MGID0_CAT128-CAT272-CAT3154,chronos2,158.97046770368303,-136.5121852329799,295.48265293666293
+CID0_SID1_PID841_MGID2_CAT10-CAT213-CAT327,chronos2,20.406714303152903,16.059158325195312,36.465872628348215
+CID0_SID1_PID843_MGID6_CAT14-CAT228-CAT3167,chronos2,31.68800844464983,-7.6905414036342075,39.37854984828404
+CID0_SID1_PID847_MGID6_CAT120-CAT250-CAT3229,chronos2,24.161021641322545,22.133512224469865,46.29453386579241
+CID0_SID1_PID858_MGID5_CAT115-CAT243-CAT311,chronos2,12.656123297555107,9.361891337803431,22.01801463535854
+CID0_SID1_PID90_MGID6_CAT14-CAT253-CAT377,chronos2,114.7718974522182,-50.39539991106306,165.16729736328125
+CID0_SID1_PID94_MGID6_CAT14-CAT228-CAT3216,chronos2,32.16720826285226,-18.842635290963308,51.00984355381557
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+CID0_SID2_PID104_MGID6_CAT120-CAT250-CAT32,chronos2,104.89169529506138,-89.20929173060826,194.10098702566964
+CID0_SID2_PID115_MGID6_CAT110-CAT233-CAT3181,chronos2,28.135685511997767,-14.337747846330915,42.473433358328684
+CID0_SID2_PID118_MGID6_CAT14-CAT228-CAT3180,chronos2,48.42266573224749,-39.19060134887695,87.61326708112443
+CID0_SID2_PID11_MGID5_CAT118-CAT280-CAT386,chronos2,15.206214087350029,1.5546643393380302,16.76087842668806
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+CID0_SID2_PID127_MGID6_CAT120-CAT258-CAT3172,chronos2,26.71504810878209,-0.06972994123186384,26.78477805001395
+CID0_SID2_PID129_MGID6_CAT110-CAT233-CAT3181,chronos2,61.18537521362305,-56.6066769191197,117.79205213274275
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+CID0_SID2_PID135_MGID6_CAT14-CAT228-CAT3167,chronos2,15.153851100376674,-7.206703458513532,22.360554558890207
+CID0_SID2_PID136_MGID6_CAT14-CAT228-CAT3161,chronos2,12.28459003993443,-9.78609139578683,22.070681435721262
+CID0_SID2_PID140_MGID6_CAT14-CAT228-CAT310,chronos2,19.474749428885325,0.7880090985979352,20.26275852748326
+CID0_SID2_PID145_MGID5_CAT118-CAT280-CAT3110,chronos2,11.441234724862236,0.33038670676095144,11.771621431623187
+CID0_SID2_PID150_MGID6_CAT18-CAT229-CAT3116,chronos2,34.016440527779714,-22.226497377668107,56.24293790544782
+CID0_SID2_PID16_MGID6_CAT18-CAT229-CAT3113,chronos2,13.900355202811104,9.712447030203682,23.612802233014786
+CID0_SID2_PID181_MGID5_CAT118-CAT280-CAT3112,chronos2,23.0193418775286,-6.988769258771624,30.008111136300222
+CID0_SID2_PID193_MGID6_CAT18-CAT229-CAT3114,chronos2,23.728163037981307,-3.7932984488351003,27.521461486816406
+CID0_SID2_PID194_MGID5_CAT118-CAT280-CAT3109,chronos2,42.51732907976423,-42.161790575299946,84.67911965506417
+CID0_SID2_PID19_MGID6_CAT14-CAT228-CAT381,chronos2,39.49704197474888,-14.022227696010045,53.51926967075893
+CID0_SID2_PID200_MGID6_CAT18-CAT229-CAT3113,chronos2,18.800874710083008,-7.823757444109235,26.62463215419224
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+CID0_SID2_PID207_MGID6_CAT14-CAT228-CAT3179,chronos2,37.675749097551616,-4.242124829973493,41.91787392752511
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+CID0_SID2_PID213_MGID6_CAT14-CAT228-CAT381,chronos2,15.454011917114258,-3.1502173287527904,18.60422924586705
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+CID0_SID8_PID90_MGID6_CAT14-CAT253-CAT377,chronos2,64.2536062513079,14.004244940621513,78.25785119192942

metrics/demand_profile.csv

@@ -0,0 +1,298 @@
+id,mean,std,T,N,ADI,CV2
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+CID0_SID1_PID796_MGID6_CAT14-CAT228-CAT31,192.3684210526316,76.1729334440632,76,74,1.027027027027027,0.1567955745273326
+CID0_SID1_PID79_MGID6_CAT14-CAT228-CAT310,54.3421052631579,31.888787008008087,76,76,1.0,0.34435237352625625
+CID0_SID1_PID7_MGID2_CAT131-CAT279-CAT3228,46.71052631578947,23.288804629105194,76,72,1.0555555555555556,0.24857925014877996
+CID0_SID1_PID806_MGID6_CAT121-CAT264-CAT3184,45.0,29.776948578836397,76,69,1.1014492753623188,0.4378600823045268
+CID0_SID1_PID810_MGID6_CAT14-CAT228-CAT3168,139.21052631578948,74.00474143178913,76,73,1.0410958904109588,0.282601810790175
+CID0_SID1_PID816_MGID4_CAT127-CAT20-CAT30,49.21052631578947,14.584298213694234,76,76,1.0,0.08783246113224091
+CID0_SID1_PID822_MGID6_CAT120-CAT250-CAT3183,53.473684210526315,32.64780694389156,76,70,1.0857142857142856,0.37275872135077603
+CID0_SID1_PID829_MGID3_CAT111-CAT235-CAT371,46.93421052631579,29.690553167100497,76,73,1.0410958904109588,0.4001819941055478
+CID0_SID1_PID830_MGID1_CAT17-CAT216-CAT344,54.473684210526315,70.47259016747064,76,60,1.2666666666666666,1.673658972982644
+CID0_SID1_PID834_MGID0_CAT128-CAT272-CAT3154,171.8421052631579,87.70724829515957,76,73,1.0410958904109588,0.26050244405410455
+CID0_SID1_PID841_MGID2_CAT10-CAT213-CAT327,55.223684210526315,27.87476693317717,76,75,1.0133333333333334,0.2547837560997681
+CID0_SID1_PID843_MGID6_CAT14-CAT228-CAT3167,94.3157894736842,33.34834749581687,76,75,1.0133333333333334,0.12502009592899668
+CID0_SID1_PID847_MGID6_CAT120-CAT250-CAT3229,56.26315789473684,29.713910736018406,76,76,1.0,0.27891471036667614
+CID0_SID1_PID858_MGID5_CAT115-CAT243-CAT311,72.72368421052632,112.00233315238995,76,74,1.027027027027027,2.371932876581431
+CID0_SID1_PID90_MGID6_CAT14-CAT253-CAT377,153.3684210526316,139.25629533157087,76,65,1.1692307692307693,0.8244375836434904
+CID0_SID1_PID94_MGID6_CAT14-CAT228-CAT3216,48.94736842105263,21.514580939038908,76,75,1.0133333333333334,0.19319998458395968
+CID0_SID1_PID99_MGID6_CAT18-CAT229-CAT3115,71.9342105263158,36.13763892907809,76,75,1.0133333333333334,0.2523763095057699
+CID0_SID1_PID9_MGID2_CAT131-CAT279-CAT3232,49.3421052631579,21.561417783673345,76,74,1.027027027027027,0.19094983111111108
+CID0_SID2_PID104_MGID6_CAT120-CAT250-CAT32,206.57894736842104,75.83407556991685,76,76,1.0,0.13475865687587057
+CID0_SID2_PID115_MGID6_CAT110-CAT233-CAT3181,70.01315789473684,50.886931766037975,76,68,1.1176470588235294,0.528266655166666
+CID0_SID2_PID118_MGID6_CAT14-CAT228-CAT3180,191.44736842105263,88.53931627427328,76,75,1.0133333333333334,0.21388164995689715
+CID0_SID2_PID11_MGID5_CAT118-CAT280-CAT386,65.39473684210526,28.679413730580766,76,75,1.0133333333333334,0.1923335047171021
+CID0_SID2_PID122_MGID6_CAT120-CAT268-CAT3127,132.82894736842104,53.56824635494296,76,76,1.0,0.16264076055416424
+CID0_SID2_PID127_MGID6_CAT120-CAT258-CAT3172,66.1842105263158,31.2395947589234,76,73,1.0410958904109588,0.22279323396927905
+CID0_SID2_PID129_MGID6_CAT110-CAT233-CAT3181,103.55263157894737,116.94675651615619,76,58,1.3103448275862069,1.275422524106523
+CID0_SID2_PID12_MGID5_CAT118-CAT280-CAT386,42.61842105263158,23.514515860216026,76,75,1.0133333333333334,0.3044229369451241
+CID0_SID2_PID135_MGID6_CAT14-CAT228-CAT3167,51.276315789473685,24.642834622778565,76,76,1.0,0.23096581117395373
+CID0_SID2_PID136_MGID6_CAT14-CAT228-CAT3161,39.21052631578947,20.380589320543006,76,72,1.0555555555555556,0.2701644070086933
+CID0_SID2_PID140_MGID6_CAT14-CAT228-CAT310,41.05263157894737,20.693248326828478,76,73,1.0410958904109588,0.2540828402366865
+CID0_SID2_PID145_MGID5_CAT118-CAT280-CAT3110,43.01315789473684,18.522414112674504,76,76,1.0,0.18543534760492064
+CID0_SID2_PID150_MGID6_CAT18-CAT229-CAT3116,51.1578947368421,27.276877940399242,76,74,1.027027027027027,0.28429157705182717
+CID0_SID2_PID16_MGID6_CAT18-CAT229-CAT3113,53.1578947368421,26.892899995638974,76,74,1.027027027027027,0.2559409208247559
+CID0_SID2_PID181_MGID5_CAT118-CAT280-CAT3112,70.94736842105263,34.11623063854587,76,75,1.0133333333333334,0.23123283349035975
+CID0_SID2_PID193_MGID6_CAT18-CAT229-CAT3114,109.60526315789474,37.03838691497184,76,75,1.0133333333333334,0.1141934805134538
+CID0_SID2_PID194_MGID5_CAT118-CAT280-CAT3109,38.51315789473684,23.09429564260556,76,73,1.0410958904109588,0.359576401622178
+CID0_SID2_PID19_MGID6_CAT14-CAT228-CAT381,146.44736842105263,56.04055549019052,76,75,1.0133333333333334,0.14643393024311502
+CID0_SID2_PID200_MGID6_CAT18-CAT229-CAT3113,40.39473684210526,21.50601710986543,76,72,1.0555555555555556,0.2834460489412797
+CID0_SID2_PID201_MGID6_CAT18-CAT229-CAT3113,101.94736842105263,41.21096771066075,76,76,1.0,0.16340810207475953
+CID0_SID2_PID207_MGID6_CAT14-CAT228-CAT3179,84.86842105263158,32.35195701664766,76,75,1.0133333333333334,0.1453144722873225
+CID0_SID2_PID212_MGID6_CAT120-CAT250-CAT3229,61.71052631578947,25.000701744537054,76,75,1.0133333333333334,0.1641292168460166
+CID0_SID2_PID213_MGID6_CAT14-CAT228-CAT381,41.39473684210526,25.821995248169557,76,75,1.0133333333333334,0.38912518639733584
+CID0_SID2_PID215_MGID6_CAT14-CAT228-CAT3149,278.6842105263158,85.26573528701368,76,76,1.0,0.09361045498467357
+CID0_SID2_PID216_MGID6_CAT121-CAT264-CAT3123,53.28947368421053,20.80949513369554,76,74,1.027027027027027,0.15248960016257684
+CID0_SID2_PID21_MGID6_CAT14-CAT228-CAT381,57.10526315789474,25.654930103130106,76,75,1.0133333333333334,0.20183170875009732
+CID0_SID2_PID220_MGID6_CAT18-CAT229-CAT3113,49.473684210526315,23.203145582275713,76,74,1.027027027027027,0.2199607665610383
+CID0_SID2_PID223_MGID6_CAT120-CAT258-CAT3172,132.3684210526316,52.15025525159986,76,76,1.0,0.15521872080966823
+CID0_SID2_PID23_MGID6_CAT14-CAT253-CAT358,61.973684210526315,33.82384708425325,76,76,1.0,0.29787316140839604
+CID0_SID2_PID240_MGID6_CAT14-CAT228-CAT3180,106.25,41.96129168650556,76,75,1.0133333333333334,0.15596955017301045
+CID0_SID2_PID250_MGID5_CAT116-CAT227-CAT397,54.671052631578945,25.212371649857264,76,76,1.0,0.21267308405774435
+CID0_SID2_PID26_MGID6_CAT14-CAT253-CAT3156,63.73684210526316,31.562580554005248,76,75,1.0133333333333334,0.24522453707334116
+CID0_SID2_PID27_MGID6_CAT14-CAT228-CAT310,52.76315789473684,42.72699955798522,76,73,1.0410958904109588,0.6557574476112293
+CID0_SID2_PID290_MGID5_CAT116-CAT225-CAT3105,124.4342105263158,43.09256255281671,76,76,1.0,0.11992923071855355
+CID0_SID2_PID291_MGID5_CAT116-CAT227-CAT398,75.13157894736842,33.21338770446365,76,76,1.0,0.19542553891483985
+CID0_SID2_PID292_MGID5_CAT116-CAT227-CAT3104,195.0921052631579,64.6465627096711,76,76,1.0,0.10980231226936639
+CID0_SID2_PID300_MGID6_CAT120-CAT250-CAT324,476.86842105263156,193.99720562284824,76,75,1.0133333333333334,0.16549840824533388
+CID0_SID2_PID321_MGID5_CAT118-CAT280-CAT3109,38.25,20.447086181980385,76,71,1.0704225352112675,0.28575903854642787
+CID0_SID2_PID345_MGID5_CAT116-CAT226-CAT396,77.72368421052632,32.42122707289593,76,76,1.0,0.1740011580471672
+CID0_SID2_PID363_MGID6_CAT14-CAT253-CAT377,119.07894736842105,59.89274038543564,76,71,1.0704225352112675,0.252975460659524
+CID0_SID2_PID379_MGID2_CAT129-CAT276-CAT3231,55.39473684210526,42.25133653029796,76,70,1.0857142857142856,0.5817600517562717
+CID0_SID2_PID41_MGID6_CAT14-CAT253-CAT377,110.85526315789474,97.7218779936023,76,64,1.1875,0.7770893814098333
+CID0_SID2_PID422_MGID5_CAT116-CAT225-CAT3101,80.65789473684211,78.42338981563753,76,76,1.0,0.9453605096038615
+CID0_SID2_PID452_MGID2_CAT129-CAT276-CAT360,39.21052631578947,21.339363182921645,76,72,1.0555555555555556,0.2961812530967075
+CID0_SID2_PID473_MGID1_CAT17-CAT217-CAT346,48.421052631578945,22.981304377193066,76,74,1.027027027027027,0.2252583490863265
+CID0_SID2_PID486_MGID6_CAT120-CAT268-CAT363,220.1315789473684,91.1773863930837,76,76,1.0,0.1715574492957842
+CID0_SID2_PID496_MGID5_CAT116-CAT225-CAT3101,42.81578947368421,17.639131139838515,76,76,1.0,0.16972525328384058
+CID0_SID2_PID4_MGID2_CAT129-CAT278-CAT382,415.6578947368421,342.3188982077611,76,76,1.0,0.6782498151252919
+CID0_SID2_PID500_MGID6_CAT14-CAT228-CAT3179,85.0,65.71656310753526,76,76,1.0,0.5977393310265281
+CID0_SID2_PID549_MGID2_CAT131-CAT279-CAT3230,124.60526315789474,56.90789727208188,76,74,1.027027027027027,0.20857982766304378
+CID0_SID2_PID554_MGID6_CAT14-CAT228-CAT3168,71.05263157894737,33.00823926914495,76,73,1.0410958904109588,0.21581636945587565
+CID0_SID2_PID556_MGID5_CAT116-CAT225-CAT394,46.578947368421055,33.72645377460439,76,74,1.027027027027027,0.5242784640428996
+CID0_SID2_PID567_MGID6_CAT120-CAT250-CAT359,95.0,43.15862215903871,76,75,1.0133333333333334,0.2063896583564174
+CID0_SID2_PID578_MGID6_CAT14-CAT253-CAT358,80.52631578947368,32.73712415966947,76,75,1.0133333333333334,0.16527432468993414
+CID0_SID2_PID580_MGID2_CAT129-CAT276-CAT360,199.47368421052633,71.5522137899703,76,76,1.0,0.12866929822729353
+CID0_SID2_PID58_MGID5_CAT115-CAT242-CAT389,52.10526315789474,26.223947807233202,76,74,1.027027027027027,0.2532986974118287
+CID0_SID2_PID600_MGID2_CAT129-CAT278-CAT3157,84.21052631578948,28.85657211773368,76,76,1.0,0.11742395833333331
+CID0_SID2_PID633_MGID5_CAT116-CAT225-CAT3105,47.89473684210526,20.219844343198044,76,75,1.0133333333333334,0.17822968240550663
+CID0_SID2_PID634_MGID5_CAT116-CAT225-CAT399,50.1578947368421,20.778227471132016,76,76,1.0,0.1716083412518484
+CID0_SID2_PID635_MGID5_CAT116-CAT227-CAT3104,59.671052631578945,33.05848883737015,76,75,1.0133333333333334,0.3069294749036346
+CID0_SID2_PID638_MGID5_CAT116-CAT225-CAT394,76.1842105263158,34.83167544111378,76,75,1.0133333333333334,0.20903489330561215
+CID0_SID2_PID644_MGID4_CAT123-CAT210-CAT317,71.97368421052632,44.000598082059604,76,75,1.0133333333333334,0.37374009471640224
+CID0_SID2_PID645_MGID6_CAT124-CAT269-CAT3139,42.61842105263158,40.49723187421189,76,75,1.0133333333333334,0.9029339152603421
+CID0_SID2_PID650_MGID6_CAT110-CAT233-CAT3160,43.26315789473684,30.325948590188183,76,64,1.1875,0.49135157854855216
+CID0_SID2_PID670_MGID2_CAT131-CAT279-CAT3227,51.578947368421055,20.268374812595603,76,76,1.0,0.15441621546577822
+CID0_SID2_PID686_MGID2_CAT10-CAT221-CAT3221,64.07894736842105,37.706856373130066,76,74,1.027027027027027,0.3462660521962538
+CID0_SID2_PID691_MGID6_CAT121-CAT264-CAT319,442.89473684210526,144.34048440613083,76,76,1.0,0.10621241365220954
+CID0_SID2_PID6_MGID6_CAT120-CAT250-CAT3170,85.26315789473684,41.391583176954434,76,76,1.0,0.23566834324035982
+CID0_SID2_PID70_MGID6_CAT14-CAT228-CAT381,407.7631578947368,143.66255540175177,76,76,1.0,0.12412841401718615
+CID0_SID2_PID715_MGID6_CAT110-CAT238-CAT3190,72.47368421052632,39.28001143387835,76,73,1.0410958904109588,0.29375315005818
+CID0_SID2_PID719_MGID6_CAT110-CAT257-CAT3205,45.88157894736842,16.73317431950727,76,76,1.0,0.13300867298853516
+CID0_SID2_PID728_MGID6_CAT120-CAT258-CAT374,67.5,34.063665882187934,76,76,1.0,0.25466849565615013
+CID0_SID2_PID76_MGID6_CAT18-CAT229-CAT3113,175.78947368421052,63.75501356274629,76,74,1.027027027027027,0.13153549189047054
+CID0_SID2_PID774_MGID6_CAT14-CAT253-CAT377,85.59210526315789,48.62761290503684,76,71,1.0704225352112675,0.32277395558669764
+CID0_SID2_PID775_MGID6_CAT14-CAT228-CAT3167,186.05263157894737,52.91701547060067,76,76,1.0,0.08089451014206295
+CID0_SID2_PID77_MGID6_CAT18-CAT229-CAT3113,47.63157894736842,23.825388198453723,76,74,1.027027027027027,0.25020156079891764
+CID0_SID2_PID783_MGID5_CAT122-CAT281-CAT356,87.63157894736842,39.895477473104854,76,74,1.027027027027027,0.20726504282059827
+CID0_SID2_PID802_MGID0_CAT15-CAT27-CAT366,122.76315789473684,121.72755025559361,76,66,1.1515151515151516,0.9831995273154898
+CID0_SID2_PID829_MGID3_CAT111-CAT235-CAT371,59.078947368421055,33.07275340130997,76,72,1.0555555555555556,0.31338283705603315
+CID0_SID2_PID834_MGID0_CAT128-CAT272-CAT3154,111.57894736842105,59.80083904827084,76,73,1.0410958904109588,0.287243384359796
+CID0_SID2_PID843_MGID6_CAT14-CAT228-CAT3167,60.6578947368421,33.67830266173517,76,68,1.1176470588235294,0.308266069392358
+CID0_SID2_PID93_MGID6_CAT121-CAT261-CAT3223,61.421052631578945,22.601040187209524,76,76,1.0,0.13540114747481877
+CID0_SID8_PID411_MGID0_CAT128-CAT272-CAT3218,36.8421052631579,36.52252369212874,76,58,1.3103448275862069,0.9827265306122449
+CID0_SID8_PID90_MGID6_CAT14-CAT253-CAT377,40.61842105263158,52.48745046332837,76,47,1.6170212765957446,1.669801631812169

metrics/lgbm_metrics.csv

@@ -0,0 +1,298 @@
+id,model,mae,bias,score
+CID0_SID0_PID104_MGID6_CAT120-CAT250-CAT32,lightgbm,127.63215440220787,-125.24479785833545,252.8769522605433
+CID0_SID0_PID117_MGID6_CAT14-CAT228-CAT31,lightgbm,125.8672291958895,76.70455708023435,202.57178627612385
+CID0_SID0_PID118_MGID6_CAT14-CAT228-CAT3180,lightgbm,50.40639173879261,18.377248139040677,68.78363987783328
+CID0_SID0_PID122_MGID6_CAT120-CAT268-CAT3127,lightgbm,118.49413008778728,-104.61542947546164,223.10955956324892
+CID0_SID0_PID127_MGID6_CAT120-CAT258-CAT3172,lightgbm,23.08745520785804,-18.253330749964206,41.34078595782225
+CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181,lightgbm,50.53009759548631,4.110547077934874,54.640644673421185
+CID0_SID0_PID136_MGID6_CAT14-CAT228-CAT3161,lightgbm,26.10428545305427,-11.653134231277201,37.757419684331474
+CID0_SID0_PID166_MGID6_CAT120-CAT250-CAT359,lightgbm,72.96347425309514,42.53856124884176,115.5020355019369
+CID0_SID0_PID18_MGID6_CAT14-CAT228-CAT3142,lightgbm,35.97933235679165,-23.89936122292039,59.87869357971204
+CID0_SID0_PID190_MGID6_CAT14-CAT228-CAT3131,lightgbm,59.419572424784754,-17.96334457613523,77.38291700091999
+CID0_SID0_PID193_MGID6_CAT18-CAT229-CAT3114,lightgbm,22.09201668948541,-5.993495810890138,28.085512500375547
+CID0_SID0_PID194_MGID5_CAT118-CAT280-CAT3109,lightgbm,35.717963991144494,-6.263894605236399,41.981858596380896
+CID0_SID0_PID19_MGID6_CAT14-CAT228-CAT381,lightgbm,53.98697226905751,-26.423933727867333,80.41090599692484
+CID0_SID0_PID201_MGID6_CAT18-CAT229-CAT3113,lightgbm,35.3556102440316,-11.20329767366228,46.55890791769389
+CID0_SID0_PID207_MGID6_CAT14-CAT228-CAT3179,lightgbm,46.74344729967688,-40.31479868433778,87.05824598401466
+CID0_SID0_PID214_MGID6_CAT18-CAT28-CAT39,lightgbm,22.77686963781661,17.199063881805564,39.975933519622174
+CID0_SID0_PID215_MGID6_CAT14-CAT228-CAT3149,lightgbm,131.6985066370489,-32.053867244089325,163.75237388113823
+CID0_SID0_PID21_MGID6_CAT14-CAT228-CAT381,lightgbm,36.580890708744924,16.308884300805204,52.88977500955013
+CID0_SID0_PID223_MGID6_CAT120-CAT258-CAT3172,lightgbm,45.62781216662534,-18.3766931521263,64.00450531875164
+CID0_SID0_PID23_MGID6_CAT14-CAT253-CAT358,lightgbm,65.04569758114894,-61.036463120322914,126.08216070147185
+CID0_SID0_PID253_MGID6_CAT110-CAT233-CAT383,lightgbm,19.551082057246568,5.253785376027848,24.804867433274417
+CID0_SID0_PID259_MGID6_CAT121-CAT264-CAT3123,lightgbm,29.8200200173585,21.463724059182066,51.28374407654057
+CID0_SID0_PID26_MGID6_CAT14-CAT253-CAT3156,lightgbm,34.81086876195078,-20.969502060064336,55.780370822015115
+CID0_SID0_PID291_MGID5_CAT116-CAT227-CAT398,lightgbm,29.3557044543016,5.73915931375362,35.09486376805522
+CID0_SID0_PID296_MGID5_CAT116-CAT225-CAT3103,lightgbm,39.43037651636944,-6.430921476777946,45.86129799314739
+CID0_SID0_PID300_MGID6_CAT120-CAT250-CAT324,lightgbm,202.80642722642747,40.0899917783427,242.89641900477017
+CID0_SID0_PID310_MGID5_CAT116-CAT225-CAT3105,lightgbm,49.41013968078404,-6.307767406310944,55.717907087094986
+CID0_SID0_PID345_MGID5_CAT116-CAT226-CAT396,lightgbm,14.32925685072189,10.20583260376098,24.535089454482872
+CID0_SID0_PID370_MGID6_CAT124-CAT266-CAT3199,lightgbm,31.018525079477282,-25.826797318616798,56.84532239809408
+CID0_SID0_PID379_MGID2_CAT129-CAT276-CAT3231,lightgbm,21.468044795503243,8.98286885145451,30.45091364695775
+CID0_SID0_PID38_MGID0_CAT15-CAT26-CAT365,lightgbm,134.09755046270774,123.37274544900444,257.4702959117122
+CID0_SID0_PID411_MGID0_CAT128-CAT272-CAT3218,lightgbm,25.663213654982,2.871267545921826,28.534481200903823
+CID0_SID0_PID419_MGID6_CAT124-CAT251-CAT3153,lightgbm,16.780414324686227,-2.0879439978515117,18.868358322537738
+CID0_SID0_PID41_MGID6_CAT14-CAT253-CAT377,lightgbm,87.7054717216564,46.33299191981785,134.03846364147424
+CID0_SID0_PID439_MGID6_CAT121-CAT261-CAT316,lightgbm,40.28541496071874,-6.507067671732197,46.792482632450934
+CID0_SID0_PID486_MGID6_CAT120-CAT268-CAT363,lightgbm,75.36434973297733,55.74206640562392,131.10641613860125
+CID0_SID0_PID489_MGID6_CAT121-CAT264-CAT3123,lightgbm,36.402851376467865,3.853849098330104,40.25670047479797
+CID0_SID0_PID496_MGID5_CAT116-CAT225-CAT3101,lightgbm,29.027286547582857,-8.819042048151967,37.846328595734825
+CID0_SID0_PID4_MGID2_CAT129-CAT278-CAT382,lightgbm,122.90414308054962,118.61165027496932,241.51579335551895
+CID0_SID0_PID500_MGID6_CAT14-CAT228-CAT3179,lightgbm,18.096913297306546,5.998329232268898,24.095242529575444
+CID0_SID0_PID548_MGID3_CAT111-CAT262-CAT3182,lightgbm,19.712191100913277,-4.018462247309075,23.73065334822235
+CID0_SID0_PID554_MGID6_CAT14-CAT228-CAT3168,lightgbm,45.54323959122922,-25.723074370525943,71.26631396175516
+CID0_SID0_PID567_MGID6_CAT120-CAT250-CAT359,lightgbm,35.65193896562161,-3.250050208211252,38.90198917383286
+CID0_SID0_PID578_MGID6_CAT14-CAT253-CAT358,lightgbm,32.731580786753895,-32.490864378222014,65.22244516497591
+CID0_SID0_PID580_MGID2_CAT129-CAT276-CAT360,lightgbm,72.13448408136823,-67.46527343871456,139.5997575200828
+CID0_SID0_PID596_MGID2_CAT129-CAT276-CAT360,lightgbm,67.07871614413534,55.2143416643216,122.29305780845695
+CID0_SID0_PID600_MGID2_CAT129-CAT278-CAT3157,lightgbm,74.50629601991352,-73.06721282385759,147.5735088437711
+CID0_SID0_PID631_MGID5_CAT116-CAT225-CAT3103,lightgbm,27.178065270826103,-12.245306167664422,39.42337143849053
+CID0_SID0_PID633_MGID5_CAT116-CAT225-CAT3105,lightgbm,18.016040561568296,0.8947975585926612,18.910838120160957
+CID0_SID0_PID635_MGID5_CAT116-CAT227-CAT3104,lightgbm,27.753282621718103,14.04192935797373,41.795211979691835
+CID0_SID0_PID638_MGID5_CAT116-CAT225-CAT394,lightgbm,16.96041267893712,10.028958109120229,26.98937078805735
+CID0_SID0_PID644_MGID4_CAT123-CAT210-CAT317,lightgbm,56.026073292509565,48.34305160113117,104.36912489364073
+CID0_SID0_PID672_MGID6_CAT110-CAT238-CAT3173,lightgbm,34.52919545581023,-6.32514694000929,40.85434239581952
+CID0_SID0_PID686_MGID2_CAT10-CAT221-CAT3221,lightgbm,15.085120708110257,4.117705172146415,19.202825880256672
+CID0_SID0_PID691_MGID6_CAT121-CAT264-CAT319,lightgbm,108.76366751754416,33.99500905303095,142.75867657057512
+CID0_SID0_PID699_MGID2_CAT130-CAT275-CAT3191,lightgbm,35.994749897233056,3.9689230338136166,39.96367293104667
+CID0_SID0_PID6_MGID6_CAT120-CAT250-CAT3170,lightgbm,40.127944264970616,-24.198887972828413,64.32683223779902
+CID0_SID0_PID70_MGID6_CAT14-CAT228-CAT381,lightgbm,136.7946280557579,-18.999446838461576,155.79407489421948
+CID0_SID0_PID712_MGID6_CAT124-CAT266-CAT3199,lightgbm,111.0830506378742,-62.23877041033968,173.32182104821388
+CID0_SID0_PID719_MGID6_CAT110-CAT257-CAT3205,lightgbm,15.667379207128002,12.758322592091544,28.425701799219546
+CID0_SID0_PID72_MGID3_CAT125-CAT271-CAT3213,lightgbm,21.038752945835654,11.298292046294577,32.33704499213023
+CID0_SID0_PID740_MGID2_CAT129-CAT276-CAT360,lightgbm,37.92939305030359,-5.642372854658853,43.57176590496244
+CID0_SID0_PID764_MGID5_CAT118-CAT280-CAT3111,lightgbm,22.81923236488364,9.384077170979046,32.20330953586269
+CID0_SID0_PID769_MGID6_CAT120-CAT268-CAT3176,lightgbm,39.46761663721866,-14.476782467834422,53.94439910505308
+CID0_SID0_PID76_MGID6_CAT18-CAT229-CAT3113,lightgbm,51.3717083683752,15.036581533360987,66.4082899017362
+CID0_SID0_PID775_MGID6_CAT14-CAT228-CAT3167,lightgbm,58.07067768952555,-57.529023329793525,115.59970101931907
+CID0_SID0_PID783_MGID5_CAT122-CAT281-CAT356,lightgbm,49.401389554115546,-46.132814868491195,95.53420442260673
+CID0_SID0_PID796_MGID6_CAT14-CAT228-CAT31,lightgbm,50.09430567166579,-31.449833362410715,81.54413903407651
+CID0_SID0_PID806_MGID6_CAT121-CAT264-CAT3184,lightgbm,23.97435612205687,-14.660356276148871,38.63471239820574
+CID0_SID0_PID810_MGID6_CAT14-CAT228-CAT3168,lightgbm,51.61309730266528,-13.19048978044277,64.80358708310806
+CID0_SID0_PID834_MGID0_CAT128-CAT272-CAT3154,lightgbm,170.99036660876808,-170.99036660876808,341.98073321753617
+CID0_SID0_PID843_MGID6_CAT14-CAT228-CAT3167,lightgbm,27.55177749587822,-11.178456508126402,38.73023400400462
+CID0_SID0_PID90_MGID6_CAT14-CAT253-CAT377,lightgbm,92.71789520482973,1.7543247097934693,94.4722199146232
+CID0_SID0_PID93_MGID6_CAT121-CAT261-CAT3223,lightgbm,32.90190918979698,-28.945502683418216,61.847411873215194
+CID0_SID12_PID129_MGID6_CAT110-CAT233-CAT3181,lightgbm,41.39334731884883,33.67611282180736,75.06946014065619
+CID0_SID12_PID41_MGID6_CAT14-CAT253-CAT377,lightgbm,30.0621944120469,-22.3414684838927,52.403662895939604
+CID0_SID12_PID768_MGID0_CAT15-CAT25-CAT36,lightgbm,94.38852678611322,9.000263930723419,103.38879071683664
+CID0_SID18_PID362_MGID1_CAT17-CAT217-CAT351,lightgbm,51.75504584863107,-40.289046064718754,92.04409191334983
+CID0_SID18_PID536_MGID0_CAT15-CAT26-CAT365,lightgbm,97.19304447404768,-33.63039548546906,130.82343995951675
+CID0_SID18_PID830_MGID1_CAT17-CAT216-CAT344,lightgbm,29.47491144230158,9.540054255406845,39.014965697708426
+CID0_SID19_PID768_MGID0_CAT15-CAT25-CAT36,lightgbm,30.326144210368845,10.699727795086272,41.02587200545511
+CID0_SID1_PID104_MGID6_CAT120-CAT250-CAT32,lightgbm,118.16531493426079,-20.4601219265243,138.6254368607851
+CID0_SID1_PID108_MGID6_CAT18-CAT229-CAT3113,lightgbm,46.60530719584783,-13.527250849800724,60.13255804564855
+CID0_SID1_PID110_MGID2_CAT131-CAT279-CAT3121,lightgbm,24.441266151269648,-7.902496341993029,32.34376249326267
+CID0_SID1_PID114_MGID6_CAT110-CAT233-CAT372,lightgbm,91.9523367058119,-44.32865227480688,136.2809889806188
+CID0_SID1_PID117_MGID6_CAT14-CAT228-CAT31,lightgbm,278.1768456378956,-165.350151496268,443.5269971341636
+CID0_SID1_PID118_MGID6_CAT14-CAT228-CAT3180,lightgbm,87.28648196318318,-47.230206470642194,134.51668843382538
+CID0_SID1_PID121_MGID2_CAT130-CAT274-CAT3195,lightgbm,30.038963657386347,-2.900520494006698,32.939484151393046
+CID0_SID1_PID122_MGID6_CAT120-CAT268-CAT3127,lightgbm,135.87329250050138,-86.92593141950262,222.799223920004
+CID0_SID1_PID127_MGID6_CAT120-CAT258-CAT3172,lightgbm,52.96576810704272,-37.23897130936442,90.20473941640714
+CID0_SID1_PID133_MGID2_CAT131-CAT277-CAT373,lightgbm,32.77082059530803,-14.932789850404365,47.7036104457124
+CID0_SID1_PID136_MGID6_CAT14-CAT228-CAT3161,lightgbm,31.69027285741284,-27.58404817162098,59.27432102903382
+CID0_SID1_PID138_MGID4_CAT127-CAT237-CAT3126,lightgbm,27.095552194341945,12.86263238111869,39.958184575460635
+CID0_SID1_PID140_MGID6_CAT14-CAT228-CAT310,lightgbm,24.16670637584348,-7.688326570274965,31.855032946118442
+CID0_SID1_PID151_MGID2_CAT131-CAT279-CAT3158,lightgbm,19.62909622987139,17.828392631321286,37.45748886119267
+CID0_SID1_PID166_MGID6_CAT120-CAT250-CAT359,lightgbm,102.807704178774,-35.25574108224091,138.06344526101492
+CID0_SID1_PID16_MGID6_CAT18-CAT229-CAT3113,lightgbm,33.62728853360398,3.4511027602500746,37.078391293854054
+CID0_SID1_PID17_MGID6_CAT120-CAT258-CAT3172,lightgbm,16.875462018690452,3.5664327869592642,20.441894805649717
+CID0_SID1_PID18_MGID6_CAT14-CAT228-CAT3142,lightgbm,51.824138649932245,-49.24059852783248,101.06473717776473
+CID0_SID1_PID190_MGID6_CAT14-CAT228-CAT3131,lightgbm,96.68728925345894,-29.56733396331116,126.2546232167701
+CID0_SID1_PID194_MGID5_CAT118-CAT280-CAT3109,lightgbm,78.7224920078856,-56.36966615496611,135.09215816285172
+CID0_SID1_PID19_MGID6_CAT14-CAT228-CAT381,lightgbm,133.54282961991413,-114.38467628967013,247.92750590958426
+CID0_SID1_PID200_MGID6_CAT18-CAT229-CAT3113,lightgbm,21.182135809790065,4.086055032362249,25.268190842152315
+CID0_SID1_PID201_MGID6_CAT18-CAT229-CAT3113,lightgbm,41.335086208191726,-32.09100895517826,73.42609516336998
+CID0_SID1_PID207_MGID6_CAT14-CAT228-CAT3179,lightgbm,81.86681737559023,-66.39641202541324,148.26322940100346
+CID0_SID1_PID213_MGID6_CAT14-CAT228-CAT381,lightgbm,26.973234941135697,-7.995436404642687,34.96867134577838
+CID0_SID1_PID214_MGID6_CAT18-CAT28-CAT39,lightgbm,24.784619914315897,-5.677597811958252,30.462217726274147
+CID0_SID1_PID215_MGID6_CAT14-CAT228-CAT3149,lightgbm,202.52684072548467,-138.86259875427558,341.38943947976026
+CID0_SID1_PID216_MGID6_CAT121-CAT264-CAT3123,lightgbm,29.83129902146817,0.8134321146255326,30.6447311360937
+CID0_SID1_PID219_MGID6_CAT18-CAT229-CAT3114,lightgbm,30.111781051316104,-2.258408564559844,32.37018961587595
+CID0_SID1_PID220_MGID6_CAT18-CAT229-CAT3113,lightgbm,27.378193493368958,-4.348557921030261,31.726751414399217
+CID0_SID1_PID23_MGID6_CAT14-CAT253-CAT358,lightgbm,100.01963038528672,-88.9412381638919,188.9608685491786
+CID0_SID1_PID247_MGID5_CAT116-CAT225-CAT394,lightgbm,31.85966896012125,-17.984150520544965,49.84381948066621
+CID0_SID1_PID249_MGID5_CAT116-CAT225-CAT3103,lightgbm,25.946014115717098,16.28875416618779,42.23476828190489
+CID0_SID1_PID250_MGID5_CAT116-CAT227-CAT397,lightgbm,18.819868614912988,-9.709297945305554,28.529166560218542
+CID0_SID1_PID258_MGID2_CAT131-CAT279-CAT3121,lightgbm,25.173779135068884,-15.988325539458895,41.16210467452778
+CID0_SID1_PID259_MGID6_CAT121-CAT264-CAT3123,lightgbm,35.927081580688586,18.63894084010968,54.56602242079826
+CID0_SID1_PID26_MGID6_CAT14-CAT253-CAT3156,lightgbm,36.750101712606195,-8.063839186328115,44.81394089893431
+CID0_SID1_PID27_MGID6_CAT14-CAT228-CAT310,lightgbm,18.254276976169784,10.451950449222426,28.70622742539221
+CID0_SID1_PID285_MGID6_CAT124-CAT251-CAT3203,lightgbm,22.995739667260185,2.7252057024957175,25.720945369755903
+CID0_SID1_PID290_MGID5_CAT116-CAT225-CAT3105,lightgbm,77.46153969365007,-9.507515478189891,86.96905517183995
+CID0_SID1_PID291_MGID5_CAT116-CAT227-CAT398,lightgbm,43.37820598925261,24.471408015831226,67.84961400508384
+CID0_SID1_PID292_MGID5_CAT116-CAT227-CAT3104,lightgbm,252.4897075376126,252.4897075376126,504.9794150752252
+CID0_SID1_PID293_MGID5_CAT116-CAT227-CAT3100,lightgbm,21.404184189283363,-7.393841267332253,28.798025456615616
+CID0_SID1_PID295_MGID5_CAT116-CAT225-CAT3106,lightgbm,25.80103031150781,-11.495307906764298,37.296338218272105
+CID0_SID1_PID296_MGID5_CAT116-CAT225-CAT3103,lightgbm,65.75898017169273,-21.10035398532163,86.85933415701436
+CID0_SID1_PID300_MGID6_CAT120-CAT250-CAT324,lightgbm,562.6093050174981,-483.58048806065034,1046.1897930781483
+CID0_SID1_PID304_MGID2_CAT131-CAT279-CAT3227,lightgbm,15.855522858399354,9.067843527430119,24.923366385829475
+CID0_SID1_PID321_MGID5_CAT118-CAT280-CAT3109,lightgbm,21.065199972066345,-16.481717027534,37.54691699960034
+CID0_SID1_PID345_MGID5_CAT116-CAT226-CAT396,lightgbm,35.89205617956927,16.438279784289055,52.33033596385833
+CID0_SID1_PID362_MGID1_CAT17-CAT217-CAT351,lightgbm,45.77494616873181,-31.85421679835535,77.62916296708715
+CID0_SID1_PID366_MGID5_CAT116-CAT225-CAT3103,lightgbm,18.12391862070587,2.237053485271141,20.36097210597701
+CID0_SID1_PID368_MGID5_CAT118-CAT280-CAT3112,lightgbm,24.928763204682816,2.525581920442901,27.454345125125716
+CID0_SID1_PID370_MGID6_CAT124-CAT266-CAT3199,lightgbm,36.83077105241912,-12.925309544452611,49.756080596871726
+CID0_SID1_PID373_MGID2_CAT131-CAT279-CAT322,lightgbm,27.881186160029674,13.359618806207981,41.240804966237654
+CID0_SID1_PID374_MGID5_CAT118-CAT245-CAT393,lightgbm,16.214080767568117,-0.6819518175292727,16.89603258509739
+CID0_SID1_PID379_MGID2_CAT129-CAT276-CAT3231,lightgbm,45.589180610519584,43.062995073198394,88.65217568371799
+CID0_SID1_PID381_MGID0_CAT128-CAT23-CAT37,lightgbm,104.63036694574375,66.09181840736952,170.72218535311328
+CID0_SID1_PID38_MGID0_CAT15-CAT26-CAT365,lightgbm,166.6990985515065,134.5417182962693,301.2408168477758
+CID0_SID1_PID411_MGID0_CAT128-CAT272-CAT3218,lightgbm,43.15470471905668,-0.6911850245928791,43.84588974364956
+CID0_SID1_PID415_MGID1_CAT17-CAT217-CAT342,lightgbm,24.110243843909554,7.995375985517865,32.10561982942742
+CID0_SID1_PID422_MGID5_CAT116-CAT225-CAT3101,lightgbm,52.54987808322302,7.807277382458088,60.35715546568111
+CID0_SID1_PID424_MGID5_CAT116-CAT227-CAT397,lightgbm,21.11683161505359,17.514109512773892,38.630941127827484
+CID0_SID1_PID439_MGID6_CAT121-CAT261-CAT316,lightgbm,59.34877910958575,44.16087859390753,103.50965770349327
+CID0_SID1_PID452_MGID2_CAT129-CAT276-CAT360,lightgbm,35.07874130708259,-12.290147608460183,47.36888891554277
+CID0_SID1_PID470_MGID5_CAT116-CAT225-CAT3101,lightgbm,16.48483602658884,12.846630501598494,29.331466528187335
+CID0_SID1_PID486_MGID6_CAT120-CAT268-CAT363,lightgbm,139.28164098367495,75.56891831900307,214.85055930267802
+CID0_SID1_PID487_MGID3_CAT111-CAT262-CAT3182,lightgbm,45.259085038892614,-42.84714962859767,88.10623466749028
+CID0_SID1_PID489_MGID6_CAT121-CAT264-CAT3123,lightgbm,49.84075913658181,14.4238236841239,64.26458282070571
+CID0_SID1_PID496_MGID5_CAT116-CAT225-CAT3101,lightgbm,24.156555390903566,-6.8865914278901466,31.043146818793712
+CID0_SID1_PID499_MGID6_CAT120-CAT268-CAT318,lightgbm,15.59299804962412,-6.7030220900311965,22.296020139655315
+CID0_SID1_PID4_MGID2_CAT129-CAT278-CAT382,lightgbm,156.68486677701952,139.78212430768627,296.4669910847058
+CID0_SID1_PID500_MGID6_CAT14-CAT228-CAT3179,lightgbm,49.076694360684364,1.662910384791237,50.7396047454756
+CID0_SID1_PID554_MGID6_CAT14-CAT228-CAT3168,lightgbm,46.999385675245584,-14.95926966607674,61.95865534132233
+CID0_SID1_PID556_MGID5_CAT116-CAT225-CAT394,lightgbm,18.502129817997467,8.6152837288773,27.117413546874765
+CID0_SID1_PID563_MGID5_CAT116-CAT225-CAT3105,lightgbm,26.337955543016854,8.519365671440683,34.857321214457535
+CID0_SID1_PID578_MGID6_CAT14-CAT253-CAT358,lightgbm,50.47925075010723,-43.24699171204223,93.72624246214946
+CID0_SID1_PID58_MGID5_CAT115-CAT242-CAT389,lightgbm,32.255265470152004,21.476878803422476,53.73214427357448
+CID0_SID1_PID592_MGID5_CAT115-CAT243-CAT311,lightgbm,23.24344241510216,4.533385315125214,27.776827730227375
+CID0_SID1_PID596_MGID2_CAT129-CAT276-CAT360,lightgbm,88.07927770988438,-16.67926206078185,104.75853977066623
+CID0_SID1_PID60_MGID6_CAT121-CAT261-CAT3224,lightgbm,24.033826754385768,-14.820119129002569,38.853945883388334
+CID0_SID1_PID622_MGID6_CAT121-CAT261-CAT3178,lightgbm,27.555708529675876,-6.947576880556935,34.50328541023281
+CID0_SID1_PID627_MGID4_CAT113-CAT21-CAT3214,lightgbm,23.64456266398572,-11.628461519185134,35.273024183170854
+CID0_SID1_PID628_MGID6_CAT121-CAT261-CAT316,lightgbm,50.30055872996915,-35.04358082387715,85.3441395538463
+CID0_SID1_PID62_MGID0_CAT128-CAT252-CAT361,lightgbm,39.6236376311406,-24.300580835555802,63.9242184666964
+CID0_SID1_PID631_MGID5_CAT116-CAT225-CAT3103,lightgbm,52.069568641855994,-21.32896271816492,73.39853136002091
+CID0_SID1_PID633_MGID5_CAT116-CAT225-CAT3105,lightgbm,16.04270241043182,-9.987417266242687,26.030119676674506
+CID0_SID1_PID634_MGID5_CAT116-CAT225-CAT399,lightgbm,25.045232476735112,-15.915838074899,40.96107055163411
+CID0_SID1_PID635_MGID5_CAT116-CAT227-CAT3104,lightgbm,52.0143634301052,-34.1302082898541,86.1445717199593
+CID0_SID1_PID636_MGID5_CAT116-CAT225-CAT3106,lightgbm,12.290998145667107,-0.023135126062269355,12.314133271729377
+CID0_SID1_PID638_MGID5_CAT116-CAT225-CAT394,lightgbm,39.74436598133788,-2.685936949532902,42.43030293087078
+CID0_SID1_PID63_MGID0_CAT128-CAT252-CAT3169,lightgbm,49.68831696697841,-26.35099375722339,76.0393107242018
+CID0_SID1_PID644_MGID4_CAT123-CAT210-CAT317,lightgbm,47.55626712298723,14.413251654759867,61.9695187777471
+CID0_SID1_PID653_MGID1_CAT17-CAT217-CAT354,lightgbm,26.303167074503573,-17.591158541856377,43.89432561635995
+CID0_SID1_PID663_MGID6_CAT110-CAT233-CAT3186,lightgbm,17.207108542980862,13.976030611220873,31.183139154201733
+CID0_SID1_PID670_MGID2_CAT131-CAT279-CAT3227,lightgbm,34.459193656972765,-22.442735514122894,56.90192917109566
+CID0_SID1_PID686_MGID2_CAT10-CAT221-CAT3221,lightgbm,39.425160488259394,3.2517843483824356,42.67694483664183
+CID0_SID1_PID691_MGID6_CAT121-CAT264-CAT319,lightgbm,264.2438989514611,-182.3571484180702,446.60104736953133
+CID0_SID1_PID6_MGID6_CAT120-CAT250-CAT3170,lightgbm,66.55032624697564,-32.97532815152953,99.52565439850517
+CID0_SID1_PID706_MGID4_CAT127-CAT20-CAT30,lightgbm,7.899236749733005,5.585036812240501,13.484273561973506
+CID0_SID1_PID70_MGID6_CAT14-CAT228-CAT381,lightgbm,193.15558029512596,-110.39934956712513,303.5549298622511
+CID0_SID1_PID711_MGID4_CAT127-CAT237-CAT3126,lightgbm,36.349380902158025,31.73717272806227,68.0865536302203
+CID0_SID1_PID717_MGID5_CAT116-CAT225-CAT3103,lightgbm,28.072355833138197,-9.027030955097215,37.099386788235414
+CID0_SID1_PID719_MGID6_CAT110-CAT257-CAT3205,lightgbm,14.239849699767843,-12.041125566507882,26.280975266275725
+CID0_SID1_PID738_MGID6_CAT14-CAT228-CAT381,lightgbm,39.36868315382487,-37.3794002447937,76.74808339861858
+CID0_SID1_PID740_MGID2_CAT129-CAT276-CAT360,lightgbm,53.44257642120811,-15.667544519118447,69.11012094032655
+CID0_SID1_PID74_MGID6_CAT121-CAT261-CAT316,lightgbm,17.684906575235154,-12.355908435957584,30.040815011192738
+CID0_SID1_PID764_MGID5_CAT118-CAT280-CAT3111,lightgbm,20.9229169556921,-10.133752302561897,31.056669258253997
+CID0_SID1_PID765_MGID5_CAT118-CAT280-CAT3112,lightgbm,16.665370963510902,0.3448704015189468,17.01024136502985
+CID0_SID1_PID768_MGID0_CAT15-CAT25-CAT36,lightgbm,55.093118983578215,0.2405498829890677,55.33366886656728
+CID0_SID1_PID769_MGID6_CAT120-CAT268-CAT3176,lightgbm,43.629657105649684,28.241994973256187,71.87165207890587
+CID0_SID1_PID76_MGID6_CAT18-CAT229-CAT3113,lightgbm,101.87977530102955,-34.63532782559276,136.5151031266223
+CID0_SID1_PID775_MGID6_CAT14-CAT228-CAT3167,lightgbm,186.57136987772893,186.57136987772893,373.14273975545785
+CID0_SID1_PID783_MGID5_CAT122-CAT281-CAT356,lightgbm,61.042087208063194,-0.41351891898801185,61.455606127051205
+CID0_SID1_PID793_MGID3_CAT114-CAT240-CAT3146,lightgbm,38.925614613475496,-20.788460016961192,59.714074630436684
+CID0_SID1_PID796_MGID6_CAT14-CAT228-CAT31,lightgbm,157.39753253113938,-97.40962100441412,254.80715353555348
+CID0_SID1_PID79_MGID6_CAT14-CAT228-CAT310,lightgbm,10.752592875471018,-1.1841096804773394,11.936702555948358
+CID0_SID1_PID7_MGID2_CAT131-CAT279-CAT3228,lightgbm,11.474242139413459,4.20823726087927,15.682479400292728
+CID0_SID1_PID806_MGID6_CAT121-CAT264-CAT3184,lightgbm,33.62208949103099,-12.927428607386483,46.54951809841747
+CID0_SID1_PID810_MGID6_CAT14-CAT228-CAT3168,lightgbm,66.42903482801542,-40.19537128541426,106.62440611342967
+CID0_SID1_PID816_MGID4_CAT127-CAT20-CAT30,lightgbm,14.030966357784475,-0.41652846521167525,14.44749482299615
+CID0_SID1_PID822_MGID6_CAT120-CAT250-CAT3183,lightgbm,38.0620732963802,-2.969356198442459,41.03142949482266
+CID0_SID1_PID829_MGID3_CAT111-CAT235-CAT371,lightgbm,18.288627628037496,7.915804246535324,26.20443187457282
+CID0_SID1_PID830_MGID1_CAT17-CAT216-CAT344,lightgbm,45.09157096770578,-12.771476315331281,57.86304728303706
+CID0_SID1_PID834_MGID0_CAT128-CAT272-CAT3154,lightgbm,165.21638871783117,-103.37297707560359,268.58936579343475
+CID0_SID1_PID841_MGID2_CAT10-CAT213-CAT327,lightgbm,25.568379432980805,14.507078927789587,40.07545836077039
+CID0_SID1_PID843_MGID6_CAT14-CAT228-CAT3167,lightgbm,40.008822405644295,-26.076778323099948,66.08560072874424
+CID0_SID1_PID847_MGID6_CAT120-CAT250-CAT3229,lightgbm,25.062796346647506,14.524704157261352,39.587500503908856
+CID0_SID1_PID858_MGID5_CAT115-CAT243-CAT311,lightgbm,131.93767561414094,131.93767561414094,263.8753512282819
+CID0_SID1_PID90_MGID6_CAT14-CAT253-CAT377,lightgbm,116.53964271316627,-46.38368663692654,162.9233293500928
+CID0_SID1_PID94_MGID6_CAT14-CAT228-CAT3216,lightgbm,34.17722197935176,-14.07903529282103,48.256257272172796
+CID0_SID1_PID99_MGID6_CAT18-CAT229-CAT3115,lightgbm,42.23058916857718,-33.88090325602109,76.11149242459828
+CID0_SID1_PID9_MGID2_CAT131-CAT279-CAT3232,lightgbm,26.189675823981236,-11.627472327187068,37.817148151168304
+CID0_SID2_PID104_MGID6_CAT120-CAT250-CAT32,lightgbm,119.6602697672416,-74.71379277518774,194.37406254242933
+CID0_SID2_PID115_MGID6_CAT110-CAT233-CAT3181,lightgbm,21.485392973110955,6.361612437358736,27.84700541046969
+CID0_SID2_PID118_MGID6_CAT14-CAT228-CAT3180,lightgbm,40.57099892890703,-17.46017999821459,58.031178927121616
+CID0_SID2_PID11_MGID5_CAT118-CAT280-CAT386,lightgbm,14.40277447907738,2.3213087882024843,16.724083267279862
+CID0_SID2_PID122_MGID6_CAT120-CAT268-CAT3127,lightgbm,107.80596175390248,-91.6727535006318,199.47871525453428
+CID0_SID2_PID127_MGID6_CAT120-CAT258-CAT3172,lightgbm,36.43981028549587,-3.9598320455062535,40.39964233100212
+CID0_SID2_PID129_MGID6_CAT110-CAT233-CAT3181,lightgbm,67.52538123827881,58.535662013541476,126.06104325182028
+CID0_SID2_PID12_MGID5_CAT118-CAT280-CAT386,lightgbm,23.39439524595903,-18.58141280116873,41.97580804712776
+CID0_SID2_PID135_MGID6_CAT14-CAT228-CAT3167,lightgbm,16.00207162504416,6.452990155466442,22.4550617805106
+CID0_SID2_PID136_MGID6_CAT14-CAT228-CAT3161,lightgbm,19.39940269904604,-4.059023336331182,23.458426035377222
+CID0_SID2_PID140_MGID6_CAT14-CAT228-CAT310,lightgbm,20.18897731185077,13.984413825888959,34.17339113773973
+CID0_SID2_PID145_MGID5_CAT118-CAT280-CAT3110,lightgbm,15.245637124338074,10.126839139013763,25.372476263351835
+CID0_SID2_PID150_MGID6_CAT18-CAT229-CAT3116,lightgbm,35.384735196675024,-15.688499452909412,51.073234649584435
+CID0_SID2_PID16_MGID6_CAT18-CAT229-CAT3113,lightgbm,15.730516392748838,1.010610045254758,16.741126438003597
+CID0_SID2_PID181_MGID5_CAT118-CAT280-CAT3112,lightgbm,21.350878602095282,-15.779097514539627,37.12997611663491
+CID0_SID2_PID193_MGID6_CAT18-CAT229-CAT3114,lightgbm,28.12947146399443,-4.052221231039214,32.18169269503365
+CID0_SID2_PID194_MGID5_CAT118-CAT280-CAT3109,lightgbm,40.93495346335533,-33.68869285017524,74.62364631353057
+CID0_SID2_PID19_MGID6_CAT14-CAT228-CAT381,lightgbm,51.6422847305305,12.568561640240038,64.21084637077054
+CID0_SID2_PID200_MGID6_CAT18-CAT229-CAT3113,lightgbm,24.70172433714074,6.601000761731757,31.302725098872497
+CID0_SID2_PID201_MGID6_CAT18-CAT229-CAT3113,lightgbm,30.289966410998336,-6.607562610555063,36.897529021553396
+CID0_SID2_PID207_MGID6_CAT14-CAT228-CAT3179,lightgbm,41.743006797388446,-1.3953923386762364,43.138399136064685
+CID0_SID2_PID212_MGID6_CAT120-CAT250-CAT3229,lightgbm,34.976645121097654,-5.289762636261606,40.26640775735926
+CID0_SID2_PID213_MGID6_CAT14-CAT228-CAT381,lightgbm,18.47697576401244,3.0314805970916945,21.508456361104134
+CID0_SID2_PID215_MGID6_CAT14-CAT228-CAT3149,lightgbm,122.56030341871158,41.9036524288044,164.463955847516
+CID0_SID2_PID216_MGID6_CAT121-CAT264-CAT3123,lightgbm,28.621682800054195,-1.7610474889720211,30.382730289026217
+CID0_SID2_PID21_MGID6_CAT14-CAT228-CAT381,lightgbm,32.34268201884625,-1.9390694781627897,34.28175149700904
+CID0_SID2_PID220_MGID6_CAT18-CAT229-CAT3113,lightgbm,15.346151055844988,-3.3130238677196826,18.65917492356467
+CID0_SID2_PID223_MGID6_CAT120-CAT258-CAT3172,lightgbm,55.02764268656772,-6.370180804621094,61.39782349118881
+CID0_SID2_PID23_MGID6_CAT14-CAT253-CAT358,lightgbm,56.51341838415893,-49.72880344921389,106.24222183337282
+CID0_SID2_PID240_MGID6_CAT14-CAT228-CAT3180,lightgbm,56.095943181461806,-31.276241378362442,87.37218455982425
+CID0_SID2_PID250_MGID5_CAT116-CAT227-CAT397,lightgbm,15.805211556072468,1.9416228669961018,17.74683442306857
+CID0_SID2_PID26_MGID6_CAT14-CAT253-CAT3156,lightgbm,20.999374892410884,-8.916882778510187,29.91625767092107
+CID0_SID2_PID27_MGID6_CAT14-CAT228-CAT310,lightgbm,20.40970488230701,18.62109325487315,39.030798137180156
+CID0_SID2_PID290_MGID5_CAT116-CAT225-CAT3105,lightgbm,56.49452440934971,25.209993982207692,81.7045183915574
+CID0_SID2_PID291_MGID5_CAT116-CAT227-CAT398,lightgbm,27.5650285788071,-3.109622560145843,30.674651138952946
+CID0_SID2_PID292_MGID5_CAT116-CAT227-CAT3104,lightgbm,80.86169636400678,80.86169636400678,161.72339272801355
+CID0_SID2_PID300_MGID6_CAT120-CAT250-CAT324,lightgbm,200.3267188435738,-147.47795835983683,347.8046772034106
+CID0_SID2_PID321_MGID5_CAT118-CAT280-CAT3109,lightgbm,21.87918712356824,15.676675959686047,37.55586308325429
+CID0_SID2_PID345_MGID5_CAT116-CAT226-CAT396,lightgbm,20.77390007045113,-5.329599017510605,26.103499087961737
+CID0_SID2_PID363_MGID6_CAT14-CAT253-CAT377,lightgbm,39.60043098872205,1.5148535392519717,41.115284527974026
+CID0_SID2_PID379_MGID2_CAT129-CAT276-CAT3231,lightgbm,34.09368083224589,30.411510627678915,64.5051914599248
+CID0_SID2_PID41_MGID6_CAT14-CAT253-CAT377,lightgbm,95.82209996726941,-95.82209996726941,191.64419993453882
+CID0_SID2_PID422_MGID5_CAT116-CAT225-CAT3101,lightgbm,38.66396809891991,6.085935028905488,44.7499031278254
+CID0_SID2_PID452_MGID2_CAT129-CAT276-CAT360,lightgbm,21.25901875435927,-2.7141087892155324,23.973127543574805
+CID0_SID2_PID473_MGID1_CAT17-CAT217-CAT346,lightgbm,21.76505214112392,-9.258764059619896,31.023816200743816
+CID0_SID2_PID486_MGID6_CAT120-CAT268-CAT363,lightgbm,70.23237488614255,13.831544881434256,84.0639197675768
+CID0_SID2_PID496_MGID5_CAT116-CAT225-CAT3101,lightgbm,25.669293139272913,-3.193388674774866,28.86268181404778
+CID0_SID2_PID4_MGID2_CAT129-CAT278-CAT382,lightgbm,149.2461895200235,145.2008344528305,294.447023972854
+CID0_SID2_PID500_MGID6_CAT14-CAT228-CAT3179,lightgbm,21.139751060446105,7.418927109872404,28.558678170318508
+CID0_SID2_PID549_MGID2_CAT131-CAT279-CAT3230,lightgbm,49.390777783980866,-20.36500293089549,69.75578071487635
+CID0_SID2_PID554_MGID6_CAT14-CAT228-CAT3168,lightgbm,40.329048499406674,4.147022229809274,44.47607072921595
+CID0_SID2_PID556_MGID5_CAT116-CAT225-CAT394,lightgbm,22.491143282768945,16.997322149103468,39.48846543187241
+CID0_SID2_PID567_MGID6_CAT120-CAT250-CAT359,lightgbm,35.331437070659256,13.027146163216031,48.35858323387529
+CID0_SID2_PID578_MGID6_CAT14-CAT253-CAT358,lightgbm,21.329958693257627,8.023451013563909,29.353409706821537
+CID0_SID2_PID580_MGID2_CAT129-CAT276-CAT360,lightgbm,108.65170605661754,-91.55099143113667,200.20269748775422
+CID0_SID2_PID58_MGID5_CAT115-CAT242-CAT389,lightgbm,21.749439186355364,-0.19677737610815832,21.946216562463523
+CID0_SID2_PID600_MGID2_CAT129-CAT278-CAT3157,lightgbm,109.55829124925303,-108.47112978473248,218.0294210339855
+CID0_SID2_PID633_MGID5_CAT116-CAT225-CAT3105,lightgbm,22.31341686274094,18.99428942379382,41.30770628653476
+CID0_SID2_PID634_MGID5_CAT116-CAT225-CAT399,lightgbm,12.85716574571394,-8.566981646454071,21.42414739216801
+CID0_SID2_PID635_MGID5_CAT116-CAT227-CAT3104,lightgbm,17.763539795665473,-5.337775023414693,23.101314819080166
+CID0_SID2_PID638_MGID5_CAT116-CAT225-CAT394,lightgbm,26.782104361015566,-16.78127124324872,43.56337560426429
+CID0_SID2_PID644_MGID4_CAT123-CAT210-CAT317,lightgbm,40.410814502506355,10.424891535671447,50.835706038177804
+CID0_SID2_PID645_MGID6_CAT124-CAT269-CAT3139,lightgbm,40.61678996336533,-28.828161241426123,69.44495120479145
+CID0_SID2_PID650_MGID6_CAT110-CAT233-CAT3160,lightgbm,27.561738100723385,16.291806625185906,43.85354472590929
+CID0_SID2_PID670_MGID2_CAT131-CAT279-CAT3227,lightgbm,24.046436282184846,12.54972353936346,36.596159821548305
+CID0_SID2_PID686_MGID2_CAT10-CAT221-CAT3221,lightgbm,19.39794499711478,-4.735684845296445,24.133629842411224
+CID0_SID2_PID691_MGID6_CAT121-CAT264-CAT319,lightgbm,140.1231731833555,-97.38569040679904,237.50886359015453
+CID0_SID2_PID6_MGID6_CAT120-CAT250-CAT3170,lightgbm,51.7435099584841,0.8860321284979936,52.62954208698209
+CID0_SID2_PID70_MGID6_CAT14-CAT228-CAT381,lightgbm,127.75842468991178,11.13698395465117,138.89540864456293
+CID0_SID2_PID715_MGID6_CAT110-CAT238-CAT3190,lightgbm,35.93197786063846,-6.336038784076865,42.268016644715324
+CID0_SID2_PID719_MGID6_CAT110-CAT257-CAT3205,lightgbm,21.821335935713012,-8.855779069194167,30.67711500490718
+CID0_SID2_PID728_MGID6_CAT120-CAT258-CAT374,lightgbm,23.60717570901868,-22.38904038573387,45.996216094752555
+CID0_SID2_PID76_MGID6_CAT18-CAT229-CAT3113,lightgbm,67.66566848454204,51.28661063095916,118.9522791155012
+CID0_SID2_PID774_MGID6_CAT14-CAT253-CAT377,lightgbm,48.925251286268654,-43.51487757035135,92.44012885662
+CID0_SID2_PID775_MGID6_CAT14-CAT228-CAT3167,lightgbm,69.34404179993292,24.774143504550132,94.11818530448306
+CID0_SID2_PID77_MGID6_CAT18-CAT229-CAT3113,lightgbm,26.376422005034637,-5.793370115006025,32.16979212004066
+CID0_SID2_PID783_MGID5_CAT122-CAT281-CAT356,lightgbm,48.735790062597054,-36.041367686814155,84.7771577494112
+CID0_SID2_PID802_MGID0_CAT15-CAT27-CAT366,lightgbm,98.63517998492439,-42.16004432881089,140.79522431373528
+CID0_SID2_PID829_MGID3_CAT111-CAT235-CAT371,lightgbm,29.784587747453152,20.382409896814472,50.166997644267624
+CID0_SID2_PID834_MGID0_CAT128-CAT272-CAT3154,lightgbm,112.64960338774172,-102.35252489145202,215.00212827919376
+CID0_SID2_PID843_MGID6_CAT14-CAT228-CAT3167,lightgbm,16.72842873599509,-2.229648804294808,18.9580775402899
+CID0_SID2_PID93_MGID6_CAT121-CAT261-CAT3223,lightgbm,22.390631258421227,-9.118831742334576,31.5094630007558
+CID0_SID8_PID411_MGID0_CAT128-CAT272-CAT3218,lightgbm,39.85439851899634,-4.058301115149029,43.91269963414537
+CID0_SID8_PID90_MGID6_CAT14-CAT253-CAT377,lightgbm,84.97302997019686,55.72219730989842,140.69522728009528

metrics/model_selection_audit.csv

@@ -0,0 +1,298 @@
+sku,regime,best_model,mae,bias,score,model_rank,note
+CID0_SID0_PID104_MGID6_CAT120-CAT250-CAT32,Low-Low,Holt,74.1446480896852,14.968214139932837,89.11286222961805,1.0,
+CID0_SID0_PID117_MGID6_CAT14-CAT228-CAT31,Low-Low,Holt,106.64931429751567,-8.760258754927106,115.4095730524428,1.0,
+CID0_SID0_PID118_MGID6_CAT14-CAT228-CAT3180,Low-Low,SeasonalExponentialSmoothingOptimized,37.389925839195385,-2.731973377354848,40.12189921655023,1.0,
+CID0_SID0_PID122_MGID6_CAT120-CAT268-CAT3127,Low-Low,HoltWinters,81.26502324370593,-56.31527439405344,137.58029763775937,1.0,
+CID0_SID0_PID127_MGID6_CAT120-CAT258-CAT3172,Low-Low,chronos2,21.079993111746653,-15.380333491734095,36.46032660348075,1.0,
+CID0_SID0_PID129_MGID6_CAT110-CAT233-CAT3181,High-High,CrostonOptimized,48.57142857142857,0.5674566205284017,49.13888519195697,1.0,
+CID0_SID0_PID136_MGID6_CAT14-CAT228-CAT3161,Low-Low,WindowAverage,26.428571428571427,-8.214285714285714,34.64285714285714,1.0,
+CID0_SID0_PID166_MGID6_CAT120-CAT250-CAT359,Low-Low,HistoricAverage,59.23684210526316,-16.0639097744361,75.30075187969926,1.0,
+CID0_SID0_PID18_MGID6_CAT14-CAT228-CAT3142,Low-Low,lightgbm,35.97933235679165,-23.89936122292039,59.87869357971204,1.0,
+CID0_SID0_PID190_MGID6_CAT14-CAT228-CAT3131,Low-Low,CrostonClassic,49.34204825931458,-6.268597479029579,55.61064573834416,1.0,
+CID0_SID0_PID193_MGID6_CAT18-CAT229-CAT3114,Low-Low,SimpleExponentialSmoothingOptimized,15.279162929739426,-0.1887166346811538,15.46787956442058,1.0,
+CID0_SID0_PID194_MGID5_CAT118-CAT280-CAT3109,Low-Low,WindowAverage,30.571428571428573,-2.2857142857142856,32.85714285714286,1.0,
+CID0_SID0_PID19_MGID6_CAT14-CAT228-CAT381,Low-Low,OptimizedTheta,34.4193010224814,0.1891462181826568,34.608447240664056,1.0,
+CID0_SID0_PID201_MGID6_CAT18-CAT229-CAT3113,Low-Low,OptimizedTheta,32.541003875379324,0.4964945223322701,33.037498397711595,1.0,
+CID0_SID0_PID207_MGID6_CAT14-CAT228-CAT3179,Low-Low,HoltWinters,30.02445657460643,-7.498327887385399,37.52278446199183,1.0,
+CID0_SID0_PID214_MGID6_CAT18-CAT28-CAT39,Low-Low,OptimizedTheta,14.072852247485752,5.621404147953186,19.69425639543893,1.0,
+CID0_SID0_PID215_MGID6_CAT14-CAT228-CAT3149,Low-Low,CrostonOptimized,62.40927697920618,0.2779182884138192,62.687195267620005,1.0,
+CID0_SID0_PID21_MGID6_CAT14-CAT228-CAT381,Low-Low,CrostonSBA,25.97889516295552,1.8522661406885987,27.831161303644112,1.0,
+CID0_SID0_PID223_MGID6_CAT120-CAT258-CAT3172,Low-Low,Theta,34.05936716481999,0.193423878962065,34.252791043782054,1.0,
+CID0_SID0_PID23_MGID6_CAT14-CAT253-CAT358,Low-Low,HistoricAverage,56.49248120300752,-38.95864661654135,95.45112781954889,1.0,
+CID0_SID0_PID253_MGID6_CAT110-CAT233-CAT383,Low-Low,HistoricAverage,15.661654135338347,4.112781954887216,19.774436090225564,1.0,
+CID0_SID0_PID259_MGID6_CAT121-CAT264-CAT3123,Low-Low,Naive,24.285714285714285,10.0,34.285714285714285,1.0,
+CID0_SID0_PID26_MGID6_CAT14-CAT253-CAT3156,Low-Low,SeasonalNaive,22.571428571428573,-5.0,27.571428571428573,1.0,
+CID0_SID0_PID291_MGID5_CAT116-CAT227-CAT398,Low-Low,RandomWalkWithDrift,23.5952380952381,0.8428571428571493,24.438095238095247,1.0,
+CID0_SID0_PID296_MGID5_CAT116-CAT225-CAT3103,Low-Low,CrostonClassic,33.357142857142854,3.6570394431168034,37.014182300259655,1.0,
+CID0_SID0_PID300_MGID6_CAT120-CAT250-CAT324,Low-Low,SeasonalExponentialSmoothingOptimized,158.20233485149535,-22.34610408391797,180.5484389354133,1.0,
+CID0_SID0_PID310_MGID5_CAT116-CAT225-CAT3105,Low-Low,HoltWinters,41.11604391360738,3.287712763836097,44.40375667744348,1.0,
+CID0_SID0_PID345_MGID5_CAT116-CAT226-CAT396,Low-Low,SimpleExponentialSmoothingOptimized,10.72955312891474,0.1068719024031636,10.836425031317903,1.0,
+CID0_SID0_PID370_MGID6_CAT124-CAT266-CAT3199,Low-Low,HistoricAverage,21.428571428571423,1.1654135338345952,22.59398496240602,1.0,
+CID0_SID0_PID379_MGID2_CAT129-CAT276-CAT3231,Low-High,Naive,18.571428571428573,4.285714285714286,22.857142857142858,1.0,
+CID0_SID0_PID38_MGID0_CAT15-CAT26-CAT365,Low-High,HistoricAverage,59.06015037593985,0.1127819548872146,59.17293233082707,1.0,
+CID0_SID0_PID411_MGID0_CAT128-CAT272-CAT3218,High-High,lightgbm,25.663213654982,2.871267545921826,28.534481200903823,1.0,
+CID0_SID0_PID419_MGID6_CAT124-CAT251-CAT3153,Low-Low,lightgbm,16.780414324686227,-2.087943997851512,18.868358322537738,1.0,
+CID0_SID0_PID41_MGID6_CAT14-CAT253-CAT377,Low-High,WindowAverage,66.42857142857143,0.7142857142857143,67.14285714285714,1.0,
+CID0_SID0_PID439_MGID6_CAT121-CAT261-CAT316,Low-High,HistoricAverage,34.45488721804511,-11.672932330827072,46.12781954887218,1.0,
+CID0_SID0_PID486_MGID6_CAT120-CAT268-CAT363,Low-Low,Naive,55.71428571428572,34.2857142857143,90.00000000000003,1.0,
+CID0_SID0_PID489_MGID6_CAT121-CAT264-CAT3123,Low-Low,Theta,25.87123872393056,0.1321586302636624,26.003397354194217,1.0,
+CID0_SID0_PID496_MGID5_CAT116-CAT225-CAT3101,Low-Low,SeasonalExponentialSmoothingOptimized,19.3126633235542,4.158897100607804,23.471560424162007,1.0,
+CID0_SID0_PID4_MGID2_CAT129-CAT278-CAT382,Low-High,RandomWalkWithDrift,76.51428571428572,-14.85714285714286,91.37142857142858,1.0,
+CID0_SID0_PID500_MGID6_CAT14-CAT228-CAT3179,Low-Low,Holt,17.14368358592582,-0.9540405931948922,18.09772417912072,1.0,
+CID0_SID0_PID548_MGID3_CAT111-CAT262-CAT3182,Low-Low,HoltWinters,15.65737376505663,0.0550086170070816,15.71238238206371,1.0,
+CID0_SID0_PID554_MGID6_CAT14-CAT228-CAT3168,Low-Low,Holt,31.06565172564761,-6.243647160492728,37.30929888614034,1.0,
+CID0_SID0_PID567_MGID6_CAT120-CAT250-CAT359,Low-Low,lightgbm,35.65193896562161,-3.250050208211252,38.90198917383286,1.0,
+CID0_SID0_PID578_MGID6_CAT14-CAT253-CAT358,Low-Low,WindowAverage,22.857142857142858,-6.428571428571429,29.285714285714285,1.0,
+CID0_SID0_PID580_MGID2_CAT129-CAT276-CAT360,Low-Low,WindowAverage,43.57142857142857,5.0,48.57142857142857,1.0,
+CID0_SID0_PID596_MGID2_CAT129-CAT276-CAT360,Low-Low,WindowAverage,30.0,2.142857142857143,32.142857142857146,1.0,
+CID0_SID0_PID600_MGID2_CAT129-CAT278-CAT3157,Low-Low,Holt,62.93792792943303,-62.2348329473036,125.17276087673662,1.0,
+CID0_SID0_PID631_MGID5_CAT116-CAT225-CAT3103,Low-Low,chronos2,22.781345094953263,-0.7412499019077846,23.52259499686105,1.0,
+CID0_SID0_PID633_MGID5_CAT116-CAT225-CAT3105,Low-Low,HoltWinters,12.320259615681854,0.6916668146335644,13.011926430315418,1.0,
+CID0_SID0_PID635_MGID5_CAT116-CAT227-CAT3104,Low-Low,SimpleExponentialSmoothingOptimized,16.739579579343303,1.4627713411173895,18.20235092046069,1.0,
+CID0_SID0_PID638_MGID5_CAT116-CAT225-CAT394,Low-Low,CrostonSBA,20.471690149300294,-0.269597526326516,20.74128767562681,1.0,
+CID0_SID0_PID644_MGID4_CAT123-CAT210-CAT317,Low-Low,HistoricAverage,27.875939849624064,-15.996240601503766,43.87218045112783,1.0,
+CID0_SID0_PID672_MGID6_CAT110-CAT238-CAT3173,Low-Low,WindowAverage,35.0,-2.142857142857143,37.142857142857146,1.0,
+CID0_SID0_PID686_MGID2_CAT10-CAT221-CAT3221,Low-Low,chronos2,9.82526125226702,0.0338145664760044,9.859075818743026,1.0,
+CID0_SID0_PID691_MGID6_CAT121-CAT264-CAT319,Low-Low,lightgbm,108.76366751754416,33.99500905303095,142.75867657057512,1.0,
+CID0_SID0_PID699_MGID2_CAT130-CAT275-CAT3191,Low-Low,SeasonalExponentialSmoothingOptimized,31.10891169553852,6.196572058535309,37.30548375407383,1.0,
+CID0_SID0_PID6_MGID6_CAT120-CAT250-CAT3170,Low-Low,WindowAverage,25.714285714285715,5.714285714285714,31.42857142857143,1.0,
+CID0_SID0_PID70_MGID6_CAT14-CAT228-CAT381,Low-Low,SeasonalExponentialSmoothingOptimized,103.821765670538,12.102737043795305,115.9245027143333,1.0,
+CID0_SID0_PID712_MGID6_CAT124-CAT266-CAT3199,Low-Low,HistoricAverage,74.49248120300751,-1.44736842105263,75.93984962406014,1.0,
+CID0_SID0_PID719_MGID6_CAT110-CAT257-CAT3205,Low-Low,CrostonOptimized,12.142857142857142,1.0146631160645256,13.157520258921668,1.0,
+CID0_SID0_PID72_MGID3_CAT125-CAT271-CAT3213,Low-Low,chronos2,17.24076243809291,0.4336934770856585,17.67445591517857,1.0,
+CID0_SID0_PID740_MGID2_CAT129-CAT276-CAT360,Low-Low,SeasonalExponentialSmoothingOptimized,38.32526938903536,-0.2363012951534204,38.56157068418878,1.0,
+CID0_SID0_PID764_MGID5_CAT118-CAT280-CAT3111,Low-Low,SimpleExponentialSmoothingOptimized,17.969954268717757,1.2182513095957346,19.188205578313493,1.0,
+CID0_SID0_PID769_MGID6_CAT120-CAT268-CAT3176,Low-Low,chronos2,24.10213197980608,-3.310508183070592,27.41264016287668,1.0,
+CID0_SID0_PID76_MGID6_CAT18-CAT229-CAT3113,Low-Low,SimpleExponentialSmoothingOptimized,34.39596513324414,-0.7717559327089246,35.16772106595307,1.0,
+CID0_SID0_PID775_MGID6_CAT14-CAT228-CAT3167,Low-Low,WindowAverage,41.42857142857143,5.357142857142855,46.785714285714285,1.0,
+CID0_SID0_PID783_MGID5_CAT122-CAT281-CAT356,Low-Low,Naive,37.142857142857146,4.285714285714286,41.42857142857143,1.0,
+CID0_SID0_PID796_MGID6_CAT14-CAT228-CAT31,Low-Low,Naive,52.85714285714285,-4.285714285714287,57.14285714285714,1.0,
+CID0_SID0_PID806_MGID6_CAT121-CAT264-CAT3184,Low-Low,WindowAverage,19.642857142857142,-3.2142857142857144,22.857142857142858,1.0,
+CID0_SID0_PID810_MGID6_CAT14-CAT228-CAT3168,Low-Low,OptimizedTheta,52.91053889501601,-2.8536583226024663,55.76419721761848,1.0,
+CID0_SID0_PID834_MGID0_CAT128-CAT272-CAT3154,Low-Low,SeasonalNaive,126.42857142857144,-107.85714285714286,234.28571428571428,1.0,
+CID0_SID0_PID843_MGID6_CAT14-CAT228-CAT3167,Low-Low,SeasonalNaive,17.857142857142858,2.142857142857143,20.0,1.0,
+CID0_SID0_PID90_MGID6_CAT14-CAT253-CAT377,Low-High,SimpleExponentialSmoothingOptimized,86.54768990031718,-4.405257873648783,90.95294777396596,1.0,
+CID0_SID0_PID93_MGID6_CAT121-CAT261-CAT3223,Low-Low,Naive,27.142857142857142,0.0,27.142857142857142,1.0,
+CID0_SID12_PID129_MGID6_CAT110-CAT233-CAT3181,High-High,HistoricAverage,22.06766917293233,1.6165413533834578,23.684210526315788,1.0,
+CID0_SID12_PID41_MGID6_CAT14-CAT253-CAT377,High-High,WindowAverage,22.142857142857142,-12.5,34.64285714285714,1.0,
+CID0_SID12_PID768_MGID0_CAT15-CAT25-CAT36,High-High,Holt,82.94153707028401,0.2969241424671369,83.23846121275115,1.0,
+CID0_SID18_PID362_MGID1_CAT17-CAT217-CAT351,High-High,HoltWinters,25.613546114677604,4.996233459541671,30.609779574219274,1.0,
+CID0_SID18_PID536_MGID0_CAT15-CAT26-CAT365,High-High,lightgbm,97.19304447404768,-33.63039548546906,130.82343995951675,1.0,
+CID0_SID18_PID830_MGID1_CAT17-CAT216-CAT344,High-High,CrostonSBA,21.51375307147087,3.8695643215766062,25.38331739304748,1.0,
+CID0_SID19_PID768_MGID0_CAT15-CAT25-CAT36,High-High,HoltWinters,30.08234890988777,-2.339070594172626,32.421419504060395,1.0,
+CID0_SID1_PID104_MGID6_CAT120-CAT250-CAT32,Low-Low,Naive,107.14285714285714,5.714285714285714,112.85714285714285,1.0,
+CID0_SID1_PID108_MGID6_CAT18-CAT229-CAT3113,Low-Low,SeasonalExponentialSmoothingOptimized,36.16417549024652,-2.3985378840583387,38.56271337430486,1.0,
+CID0_SID1_PID110_MGID2_CAT131-CAT279-CAT3121,Low-Low,HoltWinters,15.151830526698657,-1.843996332906612,16.99582685960527,1.0,
+CID0_SID1_PID114_MGID6_CAT110-CAT233-CAT372,Low-Low,WindowAverage,91.78571428571428,-3.928571428571429,95.71428571428572,1.0,
+CID0_SID1_PID117_MGID6_CAT14-CAT228-CAT31,Low-Low,HistoricAverage,181.42857142857144,12.857142857142849,194.28571428571428,1.0,
+CID0_SID1_PID118_MGID6_CAT14-CAT228-CAT3180,Low-Low,CrostonClassic,48.65533001095665,-2.269832780446334,50.92516279140299,1.0,
+CID0_SID1_PID121_MGID2_CAT130-CAT274-CAT3195,Low-High,Naive,30.0,0.0,30.0,1.0,
+CID0_SID1_PID122_MGID6_CAT120-CAT268-CAT3127,Low-Low,HoltWinters,107.38672637849012,-87.75640672553871,195.14313310402883,1.0,
+CID0_SID1_PID127_MGID6_CAT120-CAT258-CAT3172,Low-Low,Theta,47.28211688946606,-3.489980133272805,50.77209702273886,1.0,
+CID0_SID1_PID133_MGID2_CAT131-CAT277-CAT373,Low-High,HoltWinters,26.31011162054695,-4.785922451057294,31.096034071604244,1.0,
+CID0_SID1_PID136_MGID6_CAT14-CAT228-CAT3161,Low-Low,Naive,25.0,2.142857142857143,27.142857142857142,1.0,
+CID0_SID1_PID138_MGID4_CAT127-CAT237-CAT3126,Low-Low,WindowAverage,18.214285714285715,-4.642857142857143,22.857142857142858,1.0,
+CID0_SID1_PID140_MGID6_CAT14-CAT228-CAT310,Low-Low,CrostonClassic,16.80643489235802,1.9307585322204173,18.73719342457844,1.0,
+CID0_SID1_PID151_MGID2_CAT131-CAT279-CAT3158,Low-Low,Naive,10.714285714285714,0.7142857142857143,11.428571428571429,1.0,
+CID0_SID1_PID166_MGID6_CAT120-CAT250-CAT359,Low-Low,chronos2,85.53926522391183,-2.8530338832310287,88.39229910714286,1.0,
+CID0_SID1_PID16_MGID6_CAT18-CAT229-CAT3113,Low-Low,chronos2,31.12902777535575,0.9658083234514508,32.0948360988072,1.0,
+CID0_SID1_PID17_MGID6_CAT120-CAT258-CAT3172,Low-Low,lightgbm,16.875462018690452,3.566432786959264,20.44189480564972,1.0,
+CID0_SID1_PID18_MGID6_CAT14-CAT228-CAT3142,Low-Low,Holt,41.03607719741688,-27.71188559667483,68.7479627940917,1.0,
+CID0_SID1_PID190_MGID6_CAT14-CAT228-CAT3131,Low-Low,HistoricAverage,69.84962406015039,0.5263157894736565,70.37593984962405,1.0,
+CID0_SID1_PID194_MGID5_CAT118-CAT280-CAT3109,Low-Low,WindowAverage,70.14285714285714,-10.571428571428571,80.71428571428571,1.0,
+CID0_SID1_PID19_MGID6_CAT14-CAT228-CAT381,Low-Low,OptimizedTheta,66.61251434799857,-22.434159907349105,89.04667425534768,1.0,
+CID0_SID1_PID200_MGID6_CAT18-CAT229-CAT3113,Low-Low,WindowAverage,18.428571428571427,-2.0,20.428571428571427,1.0,
+CID0_SID1_PID201_MGID6_CAT18-CAT229-CAT3113,Low-Low,Theta,27.924191069029835,-2.2862332618356027,30.21042433086544,1.0,
+CID0_SID1_PID207_MGID6_CAT14-CAT228-CAT3179,Low-Low,Holt,47.12075405478341,-8.092242288500776,55.21299634328419,1.0,
+CID0_SID1_PID213_MGID6_CAT14-CAT228-CAT381,Low-Low,CrostonClassic,15.440750225980064,-0.4861769895681241,15.926927215548186,1.0,
+CID0_SID1_PID214_MGID6_CAT18-CAT28-CAT39,Low-Low,SeasonalExponentialSmoothingOptimized,18.554907125008373,-2.596491434553145,21.15139855956152,1.0,
+CID0_SID1_PID215_MGID6_CAT14-CAT228-CAT3149,Low-Low,chronos2,130.78228759765625,-15.932730538504464,146.71501813616072,1.0,
+CID0_SID1_PID216_MGID6_CAT121-CAT264-CAT3123,Low-Low,chronos2,17.586494990757533,-1.8066727774483813,19.39316776820592,1.0,
+CID0_SID1_PID219_MGID6_CAT18-CAT229-CAT3114,Low-Low,chronos2,23.760510308401926,-1.5513428279331751,25.311853136335102,1.0,
+CID0_SID1_PID220_MGID6_CAT18-CAT229-CAT3113,Low-Low,CrostonClassic,28.571428571428573,0.240431226472527,28.8118597979011,1.0,
+CID0_SID1_PID23_MGID6_CAT14-CAT253-CAT358,Low-Low,HistoricAverage,74.94172932330828,-45.91165413533834,120.85338345864662,1.0,
+CID0_SID1_PID247_MGID5_CAT116-CAT225-CAT394,Low-Low,Holt,26.13631032248044,3.516487673372871,29.65279799585331,1.0,
+CID0_SID1_PID249_MGID5_CAT116-CAT225-CAT3103,Low-Low,RandomWalkWithDrift,16.076190476190476,7.571428571428574,23.64761904761905,1.0,
+CID0_SID1_PID250_MGID5_CAT116-CAT227-CAT397,Low-Low,DynamicOptimizedTheta,19.285714285714285,-2.592724146871443,21.878438432585728,1.0,
+CID0_SID1_PID258_MGID2_CAT131-CAT279-CAT3121,Low-Low,WindowAverage,20.714285714285715,0.7142857142857143,21.42857142857143,1.0,
+CID0_SID1_PID259_MGID6_CAT121-CAT264-CAT3123,Low-Low,Naive,39.285714285714285,9.285714285714286,48.57142857142857,1.0,
+CID0_SID1_PID26_MGID6_CAT14-CAT253-CAT3156,Low-Low,HistoricAverage,35.92857142857143,0.075187969924806,36.00375939849624,1.0,
+CID0_SID1_PID27_MGID6_CAT14-CAT228-CAT310,Low-High,Theta,12.020496045308072,0.3289003179895159,12.34939636329759,1.0,
+CID0_SID1_PID285_MGID6_CAT124-CAT251-CAT3203,Low-Low,SeasonalExponentialSmoothingOptimized,22.29708324591197,0.9035097466470514,23.200592992559024,1.0,
+CID0_SID1_PID290_MGID5_CAT116-CAT225-CAT3105,Low-Low,RandomWalkWithDrift,49.28000000000001,-11.885714285714316,61.16571428571432,1.0,
+CID0_SID1_PID291_MGID5_CAT116-CAT227-CAT398,Low-Low,chronos2,34.63008989606585,16.35544150216239,50.98553139822823,1.0,
+CID0_SID1_PID292_MGID5_CAT116-CAT227-CAT3104,Low-Low,WindowAverage,53.57142857142857,13.571428571428571,67.14285714285714,1.0,
+CID0_SID1_PID293_MGID5_CAT116-CAT227-CAT3100,Low-Low,HistoricAverage,17.571428571428573,1.7857142857142858,19.357142857142858,1.0,
+CID0_SID1_PID295_MGID5_CAT116-CAT225-CAT3106,Low-Low,HoltWinters,20.86647137165124,-3.103933450019005,23.970404821670243,1.0,
+CID0_SID1_PID296_MGID5_CAT116-CAT225-CAT3103,Low-Low,CrostonSBA,43.25489734471574,-0.3585757298469372,43.61347307456268,1.0,
+CID0_SID1_PID300_MGID6_CAT120-CAT250-CAT324,Low-Low,HoltWinters,284.13809817819833,-8.717823427076626,292.85592160527494,1.0,
+CID0_SID1_PID304_MGID2_CAT131-CAT279-CAT3227,Low-Low,HistoricAverage,12.556390977443607,-0.6766917293233112,13.23308270676692,1.0,
+CID0_SID1_PID321_MGID5_CAT118-CAT280-CAT3109,Low-Low,WindowAverage,21.0,-1.5714285714285714,22.571428571428573,1.0,
+CID0_SID1_PID345_MGID5_CAT116-CAT226-CAT396,Low-Low,HoltWinters,13.492038835841036,-2.991938701762319,16.483977537603355,1.0,
+CID0_SID1_PID362_MGID1_CAT17-CAT217-CAT351,Low-High,lightgbm,45.77494616873181,-31.85421679835535,77.62916296708715,1.0,
+CID0_SID1_PID366_MGID5_CAT116-CAT225-CAT3103,Low-Low,lightgbm,18.12391862070587,2.237053485271141,20.36097210597701,1.0,
+CID0_SID1_PID368_MGID5_CAT118-CAT280-CAT3112,Low-Low,WindowAverage,15.0,-1.0714285714285714,16.071428571428573,1.0,
+CID0_SID1_PID370_MGID6_CAT124-CAT266-CAT3199,Low-Low,RandomWalkWithDrift,41.21904761904762,-5.142857142857143,46.36190476190477,1.0,
+CID0_SID1_PID373_MGID2_CAT131-CAT279-CAT322,Low-Low,HistoricAverage,27.96992481203008,-4.680451127819546,32.650375939849624,1.0,
+CID0_SID1_PID374_MGID5_CAT118-CAT245-CAT393,Low-Low,HoltWinters,9.756191492005431,-1.269861789509806,11.026053281515235,1.0,
+CID0_SID1_PID379_MGID2_CAT129-CAT276-CAT3231,Low-High,RandomWalkWithDrift,19.65714285714285,-4.0,23.65714285714285,1.0,
+CID0_SID1_PID381_MGID0_CAT128-CAT23-CAT37,Low-Low,SimpleExponentialSmoothingOptimized,49.02600817910298,36.33122859914452,85.3572367782475,1.0,
+CID0_SID1_PID38_MGID0_CAT15-CAT26-CAT365,Low-High,chronos2,72.80022185189384,-35.320929118565154,108.12115097045898,1.0,
+CID0_SID1_PID411_MGID0_CAT128-CAT272-CAT3218,Low-High,Naive,40.0,-1.4285714285714286,41.42857142857143,1.0,
+CID0_SID1_PID415_MGID1_CAT17-CAT217-CAT342,Low-Low,lightgbm,24.110243843909554,7.995375985517865,32.10561982942742,1.0,
+CID0_SID1_PID422_MGID5_CAT116-CAT225-CAT3101,Low-High,CrostonSBA,33.79631808355545,2.215684721015504,36.012002804570955,1.0,
+CID0_SID1_PID424_MGID5_CAT116-CAT227-CAT397,Low-High,CrostonClassic,11.640182444207117,-0.0527056808783774,11.692888125085494,1.0,
+CID0_SID1_PID439_MGID6_CAT121-CAT261-CAT316,Low-High,Holt,40.33501145231434,-1.903099598638293,42.23811105095263,1.0,
+CID0_SID1_PID452_MGID2_CAT129-CAT276-CAT360,Low-Low,WindowAverage,36.785714285714285,1.7857142857142858,38.57142857142857,1.0,
+CID0_SID1_PID470_MGID5_CAT116-CAT225-CAT3101,Low-Low,OptimizedTheta,11.072042363270995,0.6200975055337258,11.692139868804723,1.0,
+CID0_SID1_PID486_MGID6_CAT120-CAT268-CAT363,Low-Low,CrostonSBA,72.44933836854185,-0.0025114369358029,72.45184980547765,1.0,
+CID0_SID1_PID487_MGID3_CAT111-CAT262-CAT3182,Low-Low,lightgbm,45.25908503889261,-42.84714962859767,88.10623466749028,1.0,
+CID0_SID1_PID489_MGID6_CAT121-CAT264-CAT3123,Low-Low,HistoricAverage,55.78947368421053,-4.191729323308266,59.98120300751879,1.0,
+CID0_SID1_PID496_MGID5_CAT116-CAT225-CAT3101,Low-Low,DynamicTheta,19.685056831210805,-0.0821760643207412,19.767232895531546,1.0,
+CID0_SID1_PID499_MGID6_CAT120-CAT268-CAT318,Low-Low,HoltWinters,13.689659798887476,-2.032124719128676,15.721784518016152,1.0,
+CID0_SID1_PID4_MGID2_CAT129-CAT278-CAT382,Low-High,Holt,99.57007542029,-19.542798628578687,119.11287404886868,1.0,
+CID0_SID1_PID500_MGID6_CAT14-CAT228-CAT3179,Low-Low,HoltWinters,22.57947241619524,1.1354172050901212,23.714889621285355,1.0,
+CID0_SID1_PID554_MGID6_CAT14-CAT228-CAT3168,Low-Low,HoltWinters,47.28102786341327,-0.4042071468928847,47.68523501030616,1.0,
+CID0_SID1_PID556_MGID5_CAT116-CAT225-CAT394,Low-High,CrostonSBA,13.886182877610583,6.210617190615172,20.09680006822576,1.0,
+CID0_SID1_PID563_MGID5_CAT116-CAT225-CAT3105,Low-High,HistoricAverage,15.571428571428571,-4.035714285714286,19.607142857142858,1.0,
+CID0_SID1_PID578_MGID6_CAT14-CAT253-CAT358,Low-Low,RandomWalkWithDrift,28.057142857142857,-9.714285714285715,37.77142857142857,1.0,
+CID0_SID1_PID58_MGID5_CAT115-CAT242-CAT389,Low-Low,HistoricAverage,27.94924812030075,17.73872180451128,45.68796992481203,1.0,
+CID0_SID1_PID592_MGID5_CAT115-CAT243-CAT311,Low-Low,HistoricAverage,22.18045112781955,-4.154135338345869,26.33458646616542,1.0,
+CID0_SID1_PID596_MGID2_CAT129-CAT276-CAT360,Low-Low,chronos2,53.45385524204799,-3.3035169328962053,56.7573721749442,1.0,
+CID0_SID1_PID60_MGID6_CAT121-CAT261-CAT3224,Low-Low,HoltWinters,19.77765357177695,-0.0562527127632935,19.83390628454024,1.0,
+CID0_SID1_PID622_MGID6_CAT121-CAT261-CAT3178,Low-Low,CrostonSBA,27.86979446453844,-1.7414336598297413,29.611228124368186,1.0,
+CID0_SID1_PID627_MGID4_CAT113-CAT21-CAT3214,Low-High,HistoricAverage,19.285714285714285,4.285714285714286,23.57142857142857,1.0,
+CID0_SID1_PID628_MGID6_CAT121-CAT261-CAT316,Low-High,lightgbm,50.30055872996915,-35.04358082387715,85.3441395538463,1.0,
+CID0_SID1_PID62_MGID0_CAT128-CAT252-CAT361,Low-High,HoltWinters,32.78034452477277,-14.056822342107996,46.83716686688077,1.0,
+CID0_SID1_PID631_MGID5_CAT116-CAT225-CAT3103,Low-Low,HistoricAverage,26.101503759398497,7.28947368421052,33.39097744360902,1.0,
+CID0_SID1_PID633_MGID5_CAT116-CAT225-CAT3105,Low-Low,SeasonalExponentialSmoothingOptimized,12.140867125287892,-0.297030822011228,12.43789794729912,1.0,
+CID0_SID1_PID634_MGID5_CAT116-CAT225-CAT399,Low-Low,Holt,14.609573874210977,0.6840621903794967,15.293636064590473,1.0,
+CID0_SID1_PID635_MGID5_CAT116-CAT227-CAT3104,Low-Low,HoltWinters,19.9106020843305,1.0632351605172576,20.97383724484776,1.0,
+CID0_SID1_PID636_MGID5_CAT116-CAT225-CAT3106,Low-Low,lightgbm,12.290998145667109,-0.0231351260622693,12.314133271729377,1.0,
+CID0_SID1_PID638_MGID5_CAT116-CAT225-CAT394,Low-Low,SeasonalNaive,20.714285714285715,-0.7142857142857143,21.42857142857143,1.0,
+CID0_SID1_PID63_MGID0_CAT128-CAT252-CAT3169,High-High,WindowAverage,40.71428571428572,-22.857142857142858,63.57142857142857,1.0,
+CID0_SID1_PID644_MGID4_CAT123-CAT210-CAT317,Low-Low,DynamicTheta,26.483036243352338,2.979733634862697,29.462769878215035,1.0,
+CID0_SID1_PID653_MGID1_CAT17-CAT217-CAT354,Low-Low,RandomWalkWithDrift,19.16,-10.342857142857136,29.50285714285713,1.0,
+CID0_SID1_PID663_MGID6_CAT110-CAT233-CAT3186,Low-Low,chronos2,10.398132732936316,-0.0156463895525251,10.41377912248884,1.0,
+CID0_SID1_PID670_MGID2_CAT131-CAT279-CAT3227,Low-Low,HoltWinters,24.35920022026841,-13.521317509080728,37.88051772934914,1.0,
+CID0_SID1_PID686_MGID2_CAT10-CAT221-CAT3221,Low-Low,SeasonalExponentialSmoothingOptimized,29.895091740329573,-0.7899402779373149,30.685032018266888,1.0,
+CID0_SID1_PID691_MGID6_CAT121-CAT264-CAT319,Low-Low,WindowAverage,242.8571428571429,-82.85714285714286,325.7142857142857,1.0,
+CID0_SID1_PID6_MGID6_CAT120-CAT250-CAT3170,Low-Low,chronos2,53.31753540039063,-2.617542811802457,55.93507821219308,1.0,
+CID0_SID1_PID706_MGID4_CAT127-CAT20-CAT30,Low-Low,WindowAverage,4.285714285714286,0.0,4.285714285714286,1.0,
+CID0_SID1_PID70_MGID6_CAT14-CAT228-CAT381,Low-Low,OptimizedTheta,148.40973642672944,3.646377079635662,152.0561135063651,1.0,
+CID0_SID1_PID711_MGID4_CAT127-CAT237-CAT3126,Low-Low,HistoricAverage,17.857142857142858,3.026315789473685,20.883458646616543,1.0,
+CID0_SID1_PID717_MGID5_CAT116-CAT225-CAT3103,Low-Low,SimpleExponentialSmoothingOptimized,17.894526230043702,-0.4526021039798057,18.34712833402351,1.0,
+CID0_SID1_PID719_MGID6_CAT110-CAT257-CAT3205,Low-Low,WindowAverage,16.785714285714285,1.0714285714285714,17.857142857142858,1.0,
+CID0_SID1_PID738_MGID6_CAT14-CAT228-CAT381,Low-Low,RandomWalkWithDrift,18.23809523809524,-3.571428571428572,21.80952380952381,1.0,
+CID0_SID1_PID740_MGID2_CAT129-CAT276-CAT360,Low-Low,SeasonalExponentialSmoothingOptimized,41.348114152310856,-3.660118279010893,45.00823243132175,1.0,
+CID0_SID1_PID74_MGID6_CAT121-CAT261-CAT316,Low-Low,HoltWinters,8.666673939415025,2.9807652078785885,11.647439147293614,1.0,
+CID0_SID1_PID764_MGID5_CAT118-CAT280-CAT3111,Low-Low,WindowAverage,22.5,-0.3571428571428571,22.857142857142858,1.0,
+CID0_SID1_PID765_MGID5_CAT118-CAT280-CAT3112,Low-Low,lightgbm,16.665370963510902,0.3448704015189468,17.01024136502985,1.0,
+CID0_SID1_PID768_MGID0_CAT15-CAT25-CAT36,Low-High,SeasonalExponentialSmoothingOptimized,46.04064319155067,-4.336266775570465,50.376909967121136,1.0,
+CID0_SID1_PID769_MGID6_CAT120-CAT268-CAT3176,Low-Low,HoltWinters,32.018586880418525,10.34559156192917,42.36417844234769,1.0,
+CID0_SID1_PID76_MGID6_CAT18-CAT229-CAT3113,Low-Low,RandomWalkWithDrift,71.0,0.7142857142857143,71.71428571428571,1.0,
+CID0_SID1_PID775_MGID6_CAT14-CAT228-CAT3167,Low-Low,Theta,59.26555151333589,0.3312839890385776,59.59683550237447,1.0,
+CID0_SID1_PID783_MGID5_CAT122-CAT281-CAT356,Low-Low,OptimizedTheta,42.57097952290589,-6.277992408614874,48.848971931520765,1.0,
+CID0_SID1_PID793_MGID3_CAT114-CAT240-CAT3146,Low-High,chronos2,39.77202933175223,-5.101170131138393,44.87319946289063,1.0,
+CID0_SID1_PID796_MGID6_CAT14-CAT228-CAT31,Low-Low,DynamicTheta,111.35465409014698,1.712452715148698,113.06710680529568,1.0,
+CID0_SID1_PID79_MGID6_CAT14-CAT228-CAT310,Low-Low,lightgbm,10.752592875471018,-1.1841096804773394,11.936702555948358,1.0,
+CID0_SID1_PID7_MGID2_CAT131-CAT279-CAT3228,Low-Low,HistoricAverage,11.428571428571429,0.9962406015037584,12.424812030075188,1.0,
+CID0_SID1_PID806_MGID6_CAT121-CAT264-CAT3184,Low-Low,WindowAverage,24.285714285714285,-2.857142857142857,27.142857142857142,1.0,
+CID0_SID1_PID810_MGID6_CAT14-CAT228-CAT3168,Low-Low,WindowAverage,47.142857142857146,2.142857142857143,49.28571428571429,1.0,
+CID0_SID1_PID816_MGID4_CAT127-CAT20-CAT30,Low-Low,CrostonClassic,10.537066380827644,-1.6039879685450231,12.141054349372666,1.0,
+CID0_SID1_PID822_MGID6_CAT120-CAT250-CAT3183,Low-Low,CrostonSBA,30.67594019651509,0.4458670898913287,31.12180728640642,1.0,
+CID0_SID1_PID829_MGID3_CAT111-CAT235-CAT371,Low-Low,chronos2,13.020542825971331,-3.832501956394741,16.853044782366073,1.0,
+CID0_SID1_PID830_MGID1_CAT17-CAT216-CAT344,Low-High,CrostonClassic,26.42857142857144,-4.625413755209165,31.0539851837806,1.0,
+CID0_SID1_PID834_MGID0_CAT128-CAT272-CAT3154,Low-Low,RandomWalkWithDrift,116.95238095238096,-76.2857142857143,193.23809523809527,1.0,
+CID0_SID1_PID841_MGID2_CAT10-CAT213-CAT327,Low-Low,Naive,12.857142857142858,1.4285714285714286,14.285714285714286,1.0,
+CID0_SID1_PID843_MGID6_CAT14-CAT228-CAT3167,Low-Low,SeasonalExponentialSmoothingOptimized,26.08135381504448,-2.46783920599582,28.5491930210403,1.0,
+CID0_SID1_PID847_MGID6_CAT120-CAT250-CAT3229,Low-Low,Holt,15.072163553601351,-3.004629665819178,18.07679321942053,1.0,
+CID0_SID1_PID858_MGID5_CAT115-CAT243-CAT311,Low-High,Naive,10.714285714285714,-2.142857142857143,12.857142857142856,1.0,
+CID0_SID1_PID90_MGID6_CAT14-CAT253-CAT377,Low-High,WindowAverage,113.57142857142856,-15.357142857142858,128.92857142857142,1.0,
+CID0_SID1_PID94_MGID6_CAT14-CAT228-CAT3216,Low-Low,WindowAverage,30.0,-11.428571428571429,41.42857142857143,1.0,
+CID0_SID1_PID99_MGID6_CAT18-CAT229-CAT3115,Low-Low,WindowAverage,33.57142857142857,-13.214285714285714,46.785714285714285,1.0,
+CID0_SID1_PID9_MGID2_CAT131-CAT279-CAT3232,Low-Low,SimpleExponentialSmoothingOptimized,19.427493183539266,-2.6390832852445567,22.06657646878382,1.0,
+CID0_SID2_PID104_MGID6_CAT120-CAT250-CAT32,Low-Low,HoltWinters,54.63820730480289,0.8647721476746304,55.50297945247752,1.0,
+CID0_SID2_PID115_MGID6_CAT110-CAT233-CAT3181,Low-High,SeasonalExponentialSmoothingOptimized,19.4752965787653,-4.619005853392285,24.094302432157583,1.0,
+CID0_SID2_PID118_MGID6_CAT14-CAT228-CAT3180,Low-Low,SeasonalExponentialSmoothingOptimized,27.79538420794287,0.430361826140178,28.22574603408305,1.0,
+CID0_SID2_PID11_MGID5_CAT118-CAT280-CAT386,Low-Low,DynamicOptimizedTheta,12.142857142857142,-0.0588115745824698,12.201668717439611,1.0,
+CID0_SID2_PID122_MGID6_CAT120-CAT268-CAT3127,Low-Low,OptimizedTheta,82.41259288344945,-61.95276487452424,144.3653577579737,1.0,
+CID0_SID2_PID127_MGID6_CAT120-CAT258-CAT3172,Low-Low,chronos2,26.71504810878209,-0.0697299412318638,26.78477805001395,1.0,
+CID0_SID2_PID129_MGID6_CAT110-CAT233-CAT3181,Low-High,SeasonalExponentialSmoothingOptimized,34.87654915704686,-1.808011031766132,36.684560188813,1.0,
+CID0_SID2_PID12_MGID5_CAT118-CAT280-CAT386,Low-Low,WindowAverage,13.928571428571429,-6.071428571428571,20.0,1.0,
+CID0_SID2_PID135_MGID6_CAT14-CAT228-CAT3167,Low-Low,Theta,13.50357142857143,0.0424733183610785,13.546044746932507,1.0,
+CID0_SID2_PID136_MGID6_CAT14-CAT228-CAT3161,Low-Low,WindowAverage,8.571428571428571,-1.4285714285714286,10.0,1.0,
+CID0_SID2_PID140_MGID6_CAT14-CAT228-CAT310,Low-Low,Holt,19.07533532715331,-0.7531145799651462,19.828449907118458,1.0,
+CID0_SID2_PID145_MGID5_CAT118-CAT280-CAT3110,Low-Low,CrostonSBA,11.26037851940758,-0.2315323249306123,11.491910844338191,1.0,
+CID0_SID2_PID150_MGID6_CAT18-CAT229-CAT3116,Low-Low,SeasonalExponentialSmoothingOptimized,26.55319553651263,-16.47523342757108,43.02842896408371,1.0,
+CID0_SID2_PID16_MGID6_CAT18-CAT229-CAT3113,Low-Low,Holt,11.254634692163489,3.7738595658595,15.028494258022986,1.0,
+CID0_SID2_PID181_MGID5_CAT118-CAT280-CAT3112,Low-Low,SimpleExponentialSmoothingOptimized,22.3117987648,0.4683056393143029,22.7801044041143,1.0,
+CID0_SID2_PID193_MGID6_CAT18-CAT229-CAT3114,Low-Low,CrostonSBA,23.57452155247673,-1.0606031377600138,24.635124690236747,1.0,
+CID0_SID2_PID194_MGID5_CAT118-CAT280-CAT3109,Low-Low,Holt,35.469611585882284,-0.8410324581480749,36.31064404403036,1.0,
+CID0_SID2_PID19_MGID6_CAT14-CAT228-CAT381,Low-Low,HistoricAverage,41.8421052631579,-2.124060150375941,43.96616541353384,1.0,
+CID0_SID2_PID200_MGID6_CAT18-CAT229-CAT3113,Low-Low,HistoricAverage,16.842105263157894,-1.3909774436090256,18.23308270676692,1.0,
+CID0_SID2_PID201_MGID6_CAT18-CAT229-CAT3113,Low-Low,OptimizedTheta,23.68084860158425,0.2982575477132556,23.979106149297504,1.0,
+CID0_SID2_PID207_MGID6_CAT14-CAT228-CAT3179,Low-Low,HoltWinters,37.40488270048122,1.4252022126610078,38.83008491314222,1.0,
+CID0_SID2_PID212_MGID6_CAT120-CAT250-CAT3229,Low-Low,OptimizedTheta,21.519799349292665,0.4724272650995585,21.992226614392223,1.0,
+CID0_SID2_PID213_MGID6_CAT14-CAT228-CAT381,Low-Low,DynamicOptimizedTheta,15.010182782094304,0.0119030980743468,15.022085880168651,1.0,
+CID0_SID2_PID215_MGID6_CAT14-CAT228-CAT3149,Low-Low,SimpleExponentialSmoothingOptimized,49.556278047661486,-1.677482237798147,51.23376028545963,1.0,
+CID0_SID2_PID216_MGID6_CAT121-CAT264-CAT3123,Low-Low,CrostonSBA,27.857142857142858,0.4875910203018817,28.34473387744474,1.0,
+CID0_SID2_PID21_MGID6_CAT14-CAT228-CAT381,Low-Low,CrostonClassic,28.391283706826723,1.7019355975350428,30.093219304361767,1.0,
+CID0_SID2_PID220_MGID6_CAT18-CAT229-CAT3113,Low-Low,Holt,9.999781889175608,-1.0937263417592848,11.093508230934892,1.0,
+CID0_SID2_PID223_MGID6_CAT120-CAT258-CAT3172,Low-Low,HistoricAverage,31.80451127819549,7.368421052631589,39.17293233082708,1.0,
+CID0_SID2_PID23_MGID6_CAT14-CAT253-CAT358,Low-Low,RandomWalkWithDrift,36.285714285714285,-19.71428571428572,56.0,1.0,
+CID0_SID2_PID240_MGID6_CAT14-CAT228-CAT3180,Low-Low,Holt,52.61575447493538,-34.65429072507295,87.27004520000833,1.0,
+CID0_SID2_PID250_MGID5_CAT116-CAT227-CAT397,Low-Low,RandomWalkWithDrift,14.333333333333334,-0.7142857142857137,15.047619047619047,1.0,
+CID0_SID2_PID26_MGID6_CAT14-CAT253-CAT3156,Low-Low,CrostonClassic,17.349536384777,-2.1519487752909305,19.50148516006793,1.0,
+CID0_SID2_PID27_MGID6_CAT14-CAT228-CAT310,Low-High,DynamicOptimizedTheta,10.771452085017968,-1.114450309411487,11.885902394429454,1.0,
+CID0_SID2_PID290_MGID5_CAT116-CAT225-CAT3105,Low-Low,Naive,36.71428571428572,-0.1428571428571428,36.85714285714286,1.0,
+CID0_SID2_PID291_MGID5_CAT116-CAT227-CAT398,Low-Low,HoltWinters,21.71842462805137,1.4507702310575417,23.16919485910892,1.0,
+CID0_SID2_PID292_MGID5_CAT116-CAT227-CAT3104,Low-Low,chronos2,35.43684714181082,27.313066755022323,62.74991389683315,1.0,
+CID0_SID2_PID300_MGID6_CAT120-CAT250-CAT324,Low-Low,RandomWalkWithDrift,148.17142857142855,-45.14285714285715,193.3142857142857,1.0,
+CID0_SID2_PID321_MGID5_CAT118-CAT280-CAT3109,Low-Low,HistoricAverage,15.714285714285714,2.5357142857142856,18.25,1.0,
+CID0_SID2_PID345_MGID5_CAT116-CAT226-CAT396,Low-Low,SeasonalExponentialSmoothingOptimized,17.287960563484358,-0.3600267209296827,17.64798728441404,1.0,
+CID0_SID2_PID363_MGID6_CAT14-CAT253-CAT377,Low-Low,CrostonSBA,35.30757760323721,4.647950182758821,39.95552778599603,1.0,
+CID0_SID2_PID379_MGID2_CAT129-CAT276-CAT3231,Low-High,SeasonalNaive,25.714285714285715,5.714285714285714,31.42857142857143,1.0,
+CID0_SID2_PID41_MGID6_CAT14-CAT253-CAT377,Low-High,HistoricAverage,48.796992481203006,-34.14473684210526,82.94172932330827,1.0,
+CID0_SID2_PID422_MGID5_CAT116-CAT225-CAT3101,Low-High,WindowAverage,21.071428571428573,-1.0714285714285714,22.142857142857142,1.0,
+CID0_SID2_PID452_MGID2_CAT129-CAT276-CAT360,Low-Low,WindowAverage,20.714285714285715,-0.3571428571428571,21.071428571428573,1.0,
+CID0_SID2_PID473_MGID1_CAT17-CAT217-CAT346,Low-Low,HoltWinters,14.93687337676925,2.5308285490074303,17.46770192577668,1.0,
+CID0_SID2_PID486_MGID6_CAT120-CAT268-CAT363,Low-Low,CrostonSBA,35.6224176097496,27.89274734840929,63.51516495815889,1.0,
+CID0_SID2_PID496_MGID5_CAT116-CAT225-CAT3101,Low-Low,Holt,19.795832132959585,-5.562539923050094,25.35837205600968,1.0,
+CID0_SID2_PID4_MGID2_CAT129-CAT278-CAT382,Low-High,Naive,83.57142857142857,0.7142857142857143,84.28571428571428,1.0,
+CID0_SID2_PID500_MGID6_CAT14-CAT228-CAT3179,Low-High,chronos2,14.71936525617327,-1.6552848815917969,16.374650137765066,1.0,
+CID0_SID2_PID549_MGID2_CAT131-CAT279-CAT3230,Low-Low,CrostonClassic,32.82539421726811,0.4682630967044576,33.29365731397257,1.0,
+CID0_SID2_PID554_MGID6_CAT14-CAT228-CAT3168,Low-Low,Naive,27.857142857142858,3.571428571428572,31.42857142857143,1.0,
+CID0_SID2_PID556_MGID5_CAT116-CAT225-CAT394,Low-High,OptimizedTheta,12.2799582954029,2.912335509727456,15.192293805130356,1.0,
+CID0_SID2_PID567_MGID6_CAT120-CAT250-CAT359,Low-Low,HoltWinters,19.024203816244192,-0.966220178331302,19.99042399457549,1.0,
+CID0_SID2_PID578_MGID6_CAT14-CAT253-CAT358,Low-Low,CrostonSBA,27.50785376738525,1.7307379140175894,29.23859168140284,1.0,
+CID0_SID2_PID580_MGID2_CAT129-CAT276-CAT360,Low-Low,DynamicOptimizedTheta,73.75209396180672,10.989471723466377,84.7415656852731,1.0,
+CID0_SID2_PID58_MGID5_CAT115-CAT242-CAT389,Low-Low,CrostonSBA,18.20046337231062,-0.04837819691285,18.24884156922347,1.0,
+CID0_SID2_PID600_MGID2_CAT129-CAT278-CAT3157,Low-Low,WindowAverage,77.14285714285714,-65.71428571428571,142.85714285714283,1.0,
+CID0_SID2_PID633_MGID5_CAT116-CAT225-CAT3105,Low-Low,CrostonSBA,12.98749193726111,8.399385948847348,21.38687788610845,1.0,
+CID0_SID2_PID634_MGID5_CAT116-CAT225-CAT399,Low-Low,WindowAverage,8.214285714285714,-3.928571428571429,12.142857142857142,1.0,
+CID0_SID2_PID635_MGID5_CAT116-CAT227-CAT3104,Low-Low,WindowAverage,15.0,0.0,15.0,1.0,
+CID0_SID2_PID638_MGID5_CAT116-CAT225-CAT394,Low-Low,Holt,18.36576576022726,2.2808146193880314,20.64658037961529,1.0,
+CID0_SID2_PID644_MGID4_CAT123-CAT210-CAT317,Low-Low,CrostonClassic,24.088308774036697,0.380509280557009,24.468818054593708,1.0,
+CID0_SID2_PID645_MGID6_CAT124-CAT269-CAT3139,Low-High,WindowAverage,42.0,-8.0,50.0,1.0,
+CID0_SID2_PID650_MGID6_CAT110-CAT233-CAT3160,Low-High,CrostonClassic,21.428571428571423,0.0934907079329333,21.52206213650436,1.0,
+CID0_SID2_PID670_MGID2_CAT131-CAT279-CAT3227,Low-Low,HoltWinters,20.128835273316124,0.635264549762954,20.76409982307908,1.0,
+CID0_SID2_PID686_MGID2_CAT10-CAT221-CAT3221,Low-Low,chronos2,11.988862446376256,1.949472972324916,13.938335418701172,1.0,
+CID0_SID2_PID691_MGID6_CAT121-CAT264-CAT319,Low-Low,WindowAverage,120.0,-12.857142857142858,132.85714285714286,1.0,
+CID0_SID2_PID6_MGID6_CAT120-CAT250-CAT3170,Low-Low,HoltWinters,36.86519969485119,-1.7428289988349153,38.6080286936861,1.0,
+CID0_SID2_PID70_MGID6_CAT14-CAT228-CAT381,Low-Low,chronos2,85.5773446219308,-5.505000523158482,91.08234514508928,1.0,
+CID0_SID2_PID715_MGID6_CAT110-CAT238-CAT3190,Low-Low,CrostonClassic,34.68060358084632,-2.189316038466447,36.86991961931277,1.0,
+CID0_SID2_PID719_MGID6_CAT110-CAT257-CAT3205,Low-Low,WindowAverage,17.357142857142858,0.2142857142857142,17.571428571428573,1.0,
+CID0_SID2_PID728_MGID6_CAT120-CAT258-CAT374,Low-Low,Holt,21.46753176368835,-5.435068041454154,26.902599805142504,1.0,
+CID0_SID2_PID76_MGID6_CAT18-CAT229-CAT3113,Low-Low,Holt,43.54310996780045,-3.3986330273782235,46.94174299517867,1.0,
+CID0_SID2_PID774_MGID6_CAT14-CAT253-CAT377,Low-Low,Naive,36.42857142857143,3.571428571428572,40.0,1.0,
+CID0_SID2_PID775_MGID6_CAT14-CAT228-CAT3167,Low-Low,HistoricAverage,64.4360902255639,2.4812030075187983,66.9172932330827,1.0,
+CID0_SID2_PID77_MGID6_CAT18-CAT229-CAT3113,Low-Low,chronos2,24.279197147914346,0.2114731924874442,24.490670340401785,1.0,
+CID0_SID2_PID783_MGID5_CAT122-CAT281-CAT356,Low-Low,WindowAverage,38.92857142857143,0.3571428571428571,39.285714285714285,1.0,
+CID0_SID2_PID802_MGID0_CAT15-CAT27-CAT366,Low-High,HistoricAverage,66.74812030075189,-12.951127819548883,79.69924812030078,1.0,
+CID0_SID2_PID829_MGID3_CAT111-CAT235-CAT371,Low-Low,Naive,21.428571428571427,8.571428571428571,30.0,1.0,
+CID0_SID2_PID834_MGID0_CAT128-CAT272-CAT3154,Low-Low,Holt,54.711867319381966,-37.956205148918585,92.66807246830056,1.0,
+CID0_SID2_PID843_MGID6_CAT14-CAT228-CAT3167,Low-Low,lightgbm,16.72842873599509,-2.229648804294808,18.9580775402899,1.0,
+CID0_SID2_PID93_MGID6_CAT121-CAT261-CAT3223,Low-Low,Theta,16.598306805975977,-0.042550410171021,16.640857216146998,1.0,
+CID0_SID8_PID411_MGID0_CAT128-CAT272-CAT3218,Low-High,Holt,39.34180909139396,-0.4776973866143724,39.819506478008336,1.0,
+CID0_SID8_PID90_MGID6_CAT14-CAT253-CAT377,High-High,SeasonalExponentialSmoothingOptimized,45.79869826459655,12.41364429640701,58.21234256100355,1.0,

metrics/regime_model_performance.csv

@@ -0,0 +1,43 @@
+regime,best_model,proportion
+High-High,HoltWinters,0.18181818181818182
+High-High,WindowAverage,0.18181818181818182
+High-High,lightgbm,0.18181818181818182
+High-High,CrostonOptimized,0.09090909090909091
+High-High,CrostonSBA,0.09090909090909091
+High-High,HistoricAverage,0.09090909090909091
+High-High,Holt,0.09090909090909091
+High-High,SeasonalExponentialSmoothingOptimized,0.09090909090909091
+Low-High,HistoricAverage,0.15
+Low-High,Naive,0.125
+Low-High,WindowAverage,0.1
+Low-High,CrostonClassic,0.075
+Low-High,Holt,0.075
+Low-High,SeasonalExponentialSmoothingOptimized,0.075
+Low-High,chronos2,0.075
+Low-High,CrostonSBA,0.05
+Low-High,HoltWinters,0.05
+Low-High,RandomWalkWithDrift,0.05
+Low-High,lightgbm,0.05
+Low-High,DynamicOptimizedTheta,0.025
+Low-High,OptimizedTheta,0.025
+Low-High,SeasonalNaive,0.025
+Low-High,SimpleExponentialSmoothingOptimized,0.025
+Low-High,Theta,0.025
+Low-Low,WindowAverage,0.14227642276422764
+Low-Low,HoltWinters,0.10569105691056911
+Low-Low,HistoricAverage,0.0975609756097561
+Low-Low,Holt,0.08130081300813008
+Low-Low,chronos2,0.08130081300813008
+Low-Low,SeasonalExponentialSmoothingOptimized,0.06504065040650407
+Low-Low,CrostonSBA,0.056910569105691054
+Low-Low,Naive,0.056910569105691054
+Low-Low,CrostonClassic,0.04878048780487805
+Low-Low,RandomWalkWithDrift,0.04878048780487805
+Low-Low,lightgbm,0.04878048780487805
+Low-Low,OptimizedTheta,0.044715447154471545
+Low-Low,SimpleExponentialSmoothingOptimized,0.04065040650406504
+Low-Low,Theta,0.028455284552845527
+Low-Low,DynamicOptimizedTheta,0.016260162601626018
+Low-Low,SeasonalNaive,0.016260162601626018
+Low-Low,DynamicTheta,0.012195121951219513
+Low-Low,CrostonOptimized,0.008130081300813009

models/chronos_inference.py

@@ -0,0 +1,149 @@
+import pandas as pd
+import numpy as np
+from chronos import Chronos2Pipeline
+from pathlib import Path
+from utils.metrics import mae, bias
+
+# ---------------------------------------------------------
+# CONFIG
+# ---------------------------------------------------------
+HORIZON = 14
+
+INFERENCE_INPUT_PATH = Path("data/processed/lgbm_ready/inference/inference_train.csv")
+INFERENCE_TARGET_PATH = Path("data/processed/lgbm_ready/inference/inference_target.csv")
+
+CHRONOS_OUTPUT_PATH = Path("metrics/chronos_predictions.csv")
+CHRONOS_METRICS_PATH = Path("metrics/chronos_metrics.csv")
+
+# ---------------------------------------------------------
+# LOAD CHRONOS MODEL
+# ---------------------------------------------------------
+pipeline = Chronos2Pipeline.from_pretrained(
+    "amazon/chronos-2",
+    device_map="cpu",  # you can switch to "cuda" once your env is ready
+)
+
+# ---------------------------------------------------------
+# LOAD DATA (wide: id, 1..T)
+# ---------------------------------------------------------
+df_infer = pd.read_csv(INFERENCE_INPUT_PATH)
+df_true = pd.read_csv(INFERENCE_TARGET_PATH)
+
+# ---------------------------------------------------------
+# Helper: wide -> Chronos long format
+# ---------------------------------------------------------
+def reshape_for_chronos(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Convert wide format to Chronos format:
+        id | timestamp | target
+    We create a synthetic daily calendar starting at 2024-01-01.
+    """
+    melted = df.melt(id_vars=["id"], var_name="day", value_name="target")
+    melted["day"] = melted["day"].astype(int)
+    melted["timestamp"] = pd.to_datetime("2024-01-01") + pd.to_timedelta(
+        melted["day"], unit="D"
+    )
+    return melted[["id", "timestamp", "target"]]
+
+context_df = reshape_for_chronos(df_infer)
+truth_df = reshape_for_chronos(df_true)
+
+# ---------------------------------------------------------
+# CHRONOS INFERENCE (univariate, per-id)
+# ---------------------------------------------------------
+prediction_rows = []
+
+for id_val, df_series in context_df.groupby("id"):
+    df_series_sorted = df_series.sort_values("timestamp")
+
+    pred_df = pipeline.predict_df(
+        df_series_sorted,
+        future_df=None,
+        prediction_length=HORIZON,
+        quantile_levels=[0.1, 0.5, 0.9],
+        id_column="id",
+        timestamp_column="timestamp",
+        target="target",
+    )
+
+    # Chronos returns columns like: timestamp, target_name, predictions, 0.1, 0.5, 0.9
+    pred_df["id"] = id_val
+    pred_df["h"] = np.arange(1, HORIZON + 1)  # horizon index
+
+    prediction_rows.append(pred_df)
+
+chronos_preds = pd.concat(prediction_rows, ignore_index=True)
+print(chronos_preds.head())
+
+# ---------------------------------------------------------
+# Save predictions (rename quantile columns)
+# ---------------------------------------------------------
+chronos_preds.rename(
+    columns={
+        "0.1": "q10",
+        "0.5": "q50",
+        "0.9": "q90",
+    },
+    inplace=True,
+)
+
+chronos_preds.to_csv(CHRONOS_OUTPUT_PATH, index=False)
+print(f"Saved Chronos forecasts → {CHRONOS_OUTPUT_PATH}")
+
+# ---------------------------------------------------------
+# METRICS — align truth with horizon index h
+# ---------------------------------------------------------
+
+# Take last HORIZON days per id from truth_df, ordered by timestamp
+true_filtered = (
+    truth_df.sort_values(["id", "timestamp"])
+    .groupby("id", group_keys=False)
+    .tail(HORIZON)
+)
+
+# Assign horizon index 1..H per id (aligned with Chronos h)
+true_filtered["h"] = (
+    true_filtered.sort_values(["id", "timestamp"])
+    .groupby("id")
+    .cumcount() + 1
+)
+
+print(true_filtered.head())
+
+# Merge on id + h, use q50 as point forecast
+merged = pd.merge(
+    true_filtered[["id", "h", "target"]],
+    chronos_preds[["id", "h", "q50"]],
+    on=["id", "h"],
+    how="inner",
+)
+
+merged.rename(columns={"q50": "forecast"}, inplace=True)
+
+# ---------------------------------------------------------
+# Compute metrics per id
+# ---------------------------------------------------------
+metric_rows = []
+
+for id_val, g in merged.groupby("id"):
+    y_true = g["target"].values
+    y_pred = g["forecast"].values
+
+    m = mae(y_true, y_pred)
+    b = bias(y_true, y_pred)
+    s = m + abs(b)
+
+    metric_rows.append(
+        {
+            "id": id_val,
+            "model": "chronos2",
+            "mae": float(m),
+            "bias": float(b),
+            "score": float(s),
+        }
+    )
+
+metrics_df = pd.DataFrame(metric_rows)
+metrics_df.to_csv(CHRONOS_METRICS_PATH, index=False)
+
+print(f"Saved metrics → {CHRONOS_METRICS_PATH}")

models/combine_metrics.py

@@ -0,0 +1,14 @@
+import pandas as pd
+import numpy as np
+from pathlib import Path
+LGBM_METRICS_PATH = Path("metrics/lgbm_metrics.csv")  
+BASELINE_METRICS_PATH = Path("metrics/baseline_metrics.csv")
+CHRONOS_METRICS_PATH = Path("metrics/chronos_metrics.csv")
+COMBINED_METRICS_PATH = Path("metrics/combined_metrics.csv")
+
+lgbm_metrics = pd.read_csv(LGBM_METRICS_PATH)
+baseline_metrics = pd.read_csv(BASELINE_METRICS_PATH)
+chronos_metrics = pd.read_csv(CHRONOS_METRICS_PATH)
+combined_metrics = pd.concat([lgbm_metrics, baseline_metrics, chronos_metrics], ignore_index=True)
+combined_metrics.to_csv(COMBINED_METRICS_PATH, index=False)
+print(f"Saved combined metrics to {COMBINED_METRICS_PATH}")

models/compute_baselines.py

@@ -0,0 +1,240 @@
+from typing import Dict
+import warnings
+from pathlib import Path
+import pandas as pd
+from typing import Any
+from tqdm import tqdm
+import numpy as np
+from utils.metrics import mae, bias
+from statsforecast import StatsForecast
+from statsforecast.models import (
+    # Baselines
+    Naive,
+    SeasonalNaive,
+    RandomWalkWithDrift,
+    HistoricAverage,
+    WindowAverage,
+
+    # Exponential smoothing / Holt / Holt-Winters
+    SimpleExponentialSmoothingOptimized,
+    SeasonalExponentialSmoothingOptimized,
+    Holt,
+    HoltWinters,
+
+    # Theta family
+    Theta,
+    OptimizedTheta,
+    DynamicTheta,
+    DynamicOptimizedTheta,
+
+    # ARIMA
+    # AutoARIMA,
+
+    # Intermittent
+    CrostonClassic,
+    CrostonOptimized,
+    CrostonSBA,
+)
+
+
+### ----- Configuration ----- ###
+TRAIN_DATA_PATH = Path("data/processed/train.csv")
+TEST_DATA_PATH = Path("data/processed/test.csv")
+METRICS_PATH = Path("metrics/baseline_metrics.csv")    
+PREDICTIONS_PATH = Path("metrics/baseline_predictions.csv")
+### ------------------------- ###
+
+HORIZON = 14  # Forecast horizon
+warnings.filterwarnings("ignore")
+
+
+# Adding type hints for better code clarity and numpy style comments for documentation  
+def build_baseline_models(
+        season_length: int = 7,
+        window_size: int = 4,
+    ) -> Dict[str, Any]:
+    """Build a dictionary of baseline forecasting models.
+    Parameters
+    ----------
+    season_length : int, optional
+        Seasonality length, by default 7
+    window_size : int, optional
+        Window size for moving average models, by default 4
+    Returns
+    -------
+    Dict[str, Any]
+        Dictionary of baseline forecasting models
+    """
+
+    models= {
+        # ----------------------
+        # 1) Naive family
+        # ----------------------
+        str(Naive().__class__.__name__): Naive(),
+        str(SeasonalNaive(season_length=season_length).__class__.__name__): SeasonalNaive(season_length=season_length),
+        str(RandomWalkWithDrift().__class__.__name__): RandomWalkWithDrift(),
+        str(HistoricAverage().__class__.__name__): HistoricAverage(),
+        str(WindowAverage(window_size=window_size).__class__.__name__): WindowAverage(window_size=window_size),
+        # ----------------------
+        # 2) SES / Holt / Holt-Winters
+        # ----------------------
+        # SES ~ simple exponential smoothing
+        str(SimpleExponentialSmoothingOptimized().__class__.__name__): SimpleExponentialSmoothingOptimized(),
+        str(SeasonalExponentialSmoothingOptimized(season_length=season_length).__class__.__name__): SeasonalExponentialSmoothingOptimized(season_length=season_length),
+        # Holt: level + trend, no seasonality
+        str(Holt().__class__.__name__): Holt(), 
+        str(HoltWinters(
+            season_length=7,          # e.g. weekly seasonality for daily data # or "multiplicative"
+        ).__class__.__name__): HoltWinters(
+            season_length=7,          # e.g. weekly seasonality for daily data
+        ),
+        # ----------------------
+        # 3) Theta family
+        # ----------------------
+        str(Theta().__class__.__name__): Theta(),
+        str(OptimizedTheta().__class__.__name__): OptimizedTheta(),
+        str(DynamicTheta().__class__.__name__): DynamicTheta(),
+        str(DynamicOptimizedTheta().__class__.__name__): DynamicOptimizedTheta(),
+
+        # ----------------------
+        # 4) ARIMA baseline
+        # ----------------------
+        # str(AutoARIMA().__class__.__name__): AutoARIMA(season_length=season_length),
+
+        # ----------------------
+        # 5) Intermittent demand
+        # ----------------------
+        str(CrostonClassic().__class__.__name__): CrostonClassic(),
+        str(CrostonSBA().__class__.__name__): CrostonSBA(),
+        str(CrostonOptimized().__class__.__name__): CrostonOptimized(),
+
+    }
+
+
+    return models
+
+# Adding type hints for better code clarity and numpy style comments for documentation  
+def compute_baseline_forecasts(
+    df: pd.DataFrame,
+    models: Dict[str, Any],
+    horizon: int = HORIZON,
+) -> pd.DataFrame:
+    """
+    df: train dataframe with columns ['id', 'date', 'sales']
+    returns: long dataframe with columns
+        ['id', 'model', 'h', 'forecast']
+    """
+    results = []
+
+    sku_ids = df['id'].unique()
+    for sku_id in tqdm(sku_ids):
+        sku_data = df[df['id'] == sku_id].sort_values('date').copy()
+        if len(sku_data) <= horizon + 5:
+            continue
+
+        sku_data.rename(columns={'sales': 'y', 'id': 'unique_id'}, inplace=True)
+        # dummy calendar for StatsForecast — only order matters
+        sku_data['ds'] = pd.date_range(start='2021-01-01', periods=len(sku_data), freq='D')
+        sku_data = sku_data[['unique_id', 'ds', 'y']]
+
+        for model_name, model in models.items():
+            sf = StatsForecast(models=[model], freq='D', n_jobs=1)
+            sf.fit(sku_data)
+            forecast_df = sf.predict(h=horizon)
+
+            # StatsForecast sometimes uses short aliases for columns
+            # map model_name → column name
+            col_map = {
+                "RandomWalkWithDrift": "RWD",
+                "SimpleExponentialSmoothingOptimized": "SESOpt",
+                "SeasonalExponentialSmoothingOptimized": "SeasESOpt",
+            }
+
+            col = col_map.get(model_name, model.__class__.__name__)
+            if col not in forecast_df.columns:
+                raise ValueError(f"Column {col} not found for model {model_name}")
+
+            forecast_values = forecast_df[col].values
+
+            for step in range(horizon):
+                results.append({
+                    "id": sku_id,
+                    "model": model_name,
+                    "h": step + 1,
+                    "forecast": float(forecast_values[step]),
+                })
+
+    return pd.DataFrame(results)
+def compute_metrics(
+    test_df: pd.DataFrame,
+    forecasts_df: pd.DataFrame,
+    horizon: int = HORIZON,
+) -> pd.DataFrame:
+    """
+    test_df: ['id', 'date', 'sales', ...]
+    forecasts_df: ['id', 'model', 'h', 'forecast']
+    returns: ['id', 'model', 'mae', 'bias', 'score']
+    """
+    test_df = test_df.sort_values(["id", "date"]).copy()
+
+    # assign step index 1..H per SKU in time order
+    test_df["h"] = test_df.groupby("id").cumcount() + 1
+
+    metrics_rows = []
+    for (sku_id, model_name), g_fore in forecasts_df.groupby(["id", "model"]):
+        g_test = test_df[test_df["id"] == sku_id].copy()
+        if g_test["h"].max() < horizon:
+            # test shorter than horizon for some reason
+            continue
+
+        # align on h
+        merged = pd.merge(
+            g_test[["id", "h", "sales"]],
+            g_fore[["id", "h", "forecast"]],
+            on=["id", "h"],
+            how="inner",
+        )
+        if merged.empty:
+            continue
+
+        y_true = merged["sales"].values
+        y_pred = merged["forecast"].values
+
+        m = mae(y_true, y_pred)
+        b = bias(y_true, y_pred)
+        s = m + abs(b)
+
+        metrics_rows.append({
+            "id": sku_id,
+            "model": model_name,
+            "mae": float(m),
+            "bias": float(b),
+            "score": float(s),
+        })
+
+    return pd.DataFrame(metrics_rows)
+
+
+if __name__ == "__main__":
+    # Load data
+    train_df = pd.read_csv(TRAIN_DATA_PATH)
+    test_df = pd.read_csv(TEST_DATA_PATH)
+
+    # Build baseline models
+    baseline_models = build_baseline_models()
+
+    # Forecast from train into test horizon
+    train_forecasts = compute_baseline_forecasts(train_df, baseline_models, horizon=HORIZON)
+
+    # Save raw forecasts (for later UI)
+    PREDICTIONS_PATH.parent.mkdir(parents=True, exist_ok=True)
+    train_forecasts.to_csv(PREDICTIONS_PATH, index=False)
+
+    # Compute metrics vs test
+    metrics_df = compute_metrics(test_df, train_forecasts, horizon=HORIZON)
+    metrics_df.to_csv(METRICS_PATH, index=False)
+
+    print("Saved:")
+    print(f" - forecasts → {PREDICTIONS_PATH}")
+    print(f" - metrics   → {METRICS_PATH}")
+    

models/generate_first_insights.py

@@ -0,0 +1,288 @@
+# %%
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+df = pd.read_csv("metrics/best_models.csv")
+
+# global win rate
+print(df["best_model"].value_counts(normalize=True).round(2))
+
+
+# %%
+full = pd.read_csv("data/processed/train.csv")
+
+vol = full.groupby("id")["sales"].agg(["mean","std"]).reset_index()
+vol["cv"] = vol["std"] / (vol["mean"] + 1e-9)
+
+best = df.merge(vol[["id","cv"]], on="id")
+
+best["cv_bin"] = pd.qcut(best["cv"], 3, labels=["Low","Mid","High"])
+print(best.groupby(["cv_bin","best_model"]).size())
+
+# %%
+df = pd.read_csv("data/processed/train.csv")
+
+g = df.groupby("id")["sales"]
+summary = g.agg(["mean","std","count"])
+summary = summary.rename(columns={"count":"T"})
+
+summary["N"] = g.apply(lambda x: (x>0).sum())
+summary["ADI"] = summary["T"] / summary["N"].replace(0,1)
+summary["CV2"] = (summary["std"]/summary["mean"].replace(0,1))**2
+
+summary.to_csv("metrics/demand_profile.csv")
+
+
+# %%
+summary["ADI_class"] = np.where(summary["ADI"] > 1.32, "High", "Low")
+summary["CV2_class"] = np.where(summary["CV2"] > 0.49, "High", "Low")
+
+summary["regime"] = summary["ADI_class"] + "-" + summary["CV2_class"]
+
+# %%
+best = pd.read_csv("metrics/best_models.csv")
+merged = best.merge(summary[["ADI","CV2","regime"]], on="id", how="left")
+
+# %%
+merged.groupby("regime")["best_model"].value_counts(normalize=True).to_csv("metrics/regime_model_performance.csv")
+
+# %%
+merged.groupby('best_model').size()
+
+# %%
+merged.groupby('regime')['best_model'].value_counts()
+
+# %%
+merged.to_csv('metrics/best_by_sku.csv')
+
+# %% [markdown]
+# ##  Key Insight
+
+
+# %%
+print(best["best_model"].value_counts(normalize=True).round(2))
+
+# %%
+m = pd.read_csv("metrics/combined_metrics.csv")
+print(m.groupby("model")["score"].mean().sort_values())
+
+# %%
+best[['id','best_model']]
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+# model_scores: Series indexed by model name, values = mean score
+model_scores = m.groupby("model")["score"].mean().sort_values()
+
+fig, ax = plt.subplots(figsize=(16, 10))
+
+# -------------------------------
+# Identify winner + tier bounds
+# -------------------------------
+best_model = model_scores.index[0]
+best_value = model_scores.iloc[0]
+
+# Tier boundaries (keep tunable)
+tierB_upper = 70   # top band of "stable enough"
+tierC_upper = 80   # start of "avoid if possible"
+
+colors = []
+for model, score in model_scores.items():
+    if model == best_model:
+        colors.append("#0047AB")        # Deep Royal Blue → portfolio winner
+    elif score < tierB_upper:
+        colors.append("#888888")        # Neutral Grey → stable / acceptable
+    else:
+        colors.append("#C43131")        # Executive Red → high-noise tier
+
+bars = ax.bar(model_scores.index, model_scores.values, color=colors)
+
+# -------------------------------
+# Threshold reference lines
+# -------------------------------
+ax.axhline(tierB_upper, color="#666666", linestyle="--", linewidth=1)
+ax.text(
+    len(model_scores) - 0.3,
+    tierB_upper + 0.8,
+    "Stable Signal Threshold",
+    color="#444444",
+    fontsize=10,
+    ha="right"
+)
+
+ax.axhline(tierC_upper, color="#444444", linestyle=":", linewidth=1)
+ax.text(
+    len(model_scores) - 0.3,
+    tierC_upper + 0.8,
+    "High-Noise Zone (Avoid for planning)",
+    color="#444444",
+    fontsize=10,
+    ha="right"
+)
+
+# -------------------------------
+# Value annotations
+# -------------------------------
+for bar, value in zip(bars, model_scores.values):
+    ax.text(
+        bar.get_x() + bar.get_width() / 2,
+        value + 0.8,
+        f"{value:.2f}",
+        ha="center",
+        fontsize=9,
+        fontweight="bold" if value == best_value else "normal"
+    )
+
+# -------------------------------
+# Styling
+# -------------------------------
+ax.set_title(
+    "Portfolio-Level Model Performance\n(Lower Score = More Stable, Lower Error Risk)",
+    fontsize=18,
+    fontweight="bold",
+    pad=16,
+)
+
+ax.set_ylabel("Mean Score (MAE + |Bias|)", fontsize=12)
+ax.set_xlabel("Forecasting Models", fontsize=12)
+
+# Fix tick warning: set ticks explicitly, then labels
+x_positions = np.arange(len(model_scores))
+ax.set_xticks(x_positions)
+ax.set_xticklabels(model_scores.index, rotation=45, ha="right")
+
+# Clean look
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+ax.grid(axis="y", alpha=0.25)
+
+plt.tight_layout()
+plt.savefig("docs/model_score_ranking.png", dpi=300, bbox_inches="tight")
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+# Tier boundaries (tunable later)
+tierB_upper = 70
+tierC_upper = 80
+
+# -------------------------------------------------
+# PREP
+# -------------------------------------------------
+model_scores = m.groupby("model")["score"].mean().sort_values()
+
+best_model = model_scores.index[0]
+best_value = model_scores.iloc[0]
+
+# Executive tiers
+
+
+# Executive-friendly group labels
+stable_models = model_scores[model_scores < tierB_upper].index
+secondary_models = model_scores[(model_scores >= tierB_upper) & (model_scores < tierC_upper)].index
+high_noise_models = model_scores[model_scores >= tierC_upper].index
+
+def group_color(model):
+    if model == best_model:
+        return "#0047AB"             # Royal blue highlight
+    elif model in stable_models:
+        return "#4C8BBF"             # Soft blue-grey (Stable)
+    elif model in secondary_models:
+        return "#A5A5A5"             # Neutral grey (Secondary)
+    else:
+        return "#C43131"             # Executive red (High noise)
+
+colors = [group_color(m) for m in model_scores.index]
+
+# -------------------------------------------------
+# PLOT
+# -------------------------------------------------
+fig, ax = plt.subplots(figsize=(14, 8))
+
+bars = ax.bar(model_scores.index, model_scores.values, color=colors)
+
+# -------------------------------------------------
+# Threshold lines with simpler executive labels
+# -------------------------------------------------
+ax.axhline(tierB_upper, color="#666666", linestyle="--", linewidth=1)
+ax.text(
+    -0.3, tierB_upper + 0.5,
+    "Stable Signal Threshold",
+    fontsize=10, color="#444444", ha="left"
+)
+
+ax.axhline(tierC_upper, color="#888888", linestyle=":", linewidth=1)
+ax.text(
+    -0.3, tierC_upper + 0.5,
+    "High-Noise Zone",
+    fontsize=10, color="#444444", ha="left"
+)
+
+# -------------------------------------------------
+# SIMPLIFIED VALUE LABELS
+# -------------------------------------------------
+for bar, value in zip(bars, model_scores.values):
+    if value < tierB_upper:
+        label_color = "#002147"
+    elif value < tierC_upper:
+        label_color = "#444444"
+    else:
+        label_color = "#7A0000"
+
+    ax.text(
+        bar.get_x() + bar.get_width()/2,
+        value + 0.5,
+        f"{value:.2f}",
+        ha="center",
+        fontsize=9,
+        color=label_color
+    )
+
+# -------------------------------------------------
+# EXECUTIVE STYLING
+# -------------------------------------------------
+ax.set_title(
+    "Forecast Model Stability Comparison\n(lower = more stable, lower risk)",
+    fontsize=18,
+    fontweight="bold",
+    pad=20
+)
+
+ax.set_ylabel("Stability Score (MAE + |Bias|)", fontsize=12)
+
+# Remove clutter by hiding long model names
+ax.set_xticks([])
+ax.set_xlabel("Model Families (ranked left → right)", fontsize=12)
+
+# Add grouped legend explanation
+group_handles = [
+    plt.Rectangle((0,0),1,1, color="#4C8BBF"),
+    plt.Rectangle((0,0),1,1, color="#A5A5A5"),
+    plt.Rectangle((0,0),1,1, color="#C43131"),
+    plt.Rectangle((0,0),1,1, color="#0047AB"),
+]
+
+ax.legend(
+    group_handles,
+    ["Stable Models", "Secondary Models", "High-Noise Models", "Best Performer"],
+    frameon=False,
+    loc="upper left"
+)
+
+# Clean look
+ax.spines["top"].set_visible(False)
+ax.spines["right"].set_visible(False)
+ax.grid(axis="y", alpha=0.25)
+
+plt.tight_layout()
+plt.savefig("docs/model_score_ranking_exec.png", dpi=300, bbox_inches="tight")
+plt.show()

models/lgbm_modeling.py

@@ -0,0 +1,147 @@
+import numpy as np
+import pandas as pd
+from lightgbm import LGBMRegressor
+from sklearn.multioutput import MultiOutputRegressor
+from utils.metrics import mae, bias
+from pathlib import Path
+
+# ---------------------------------------------------------
+# CONFIG
+# ---------------------------------------------------------
+HORIZON = 14  # FreshRetailNet setup: 14-step forecast
+
+TRAIN_DATA_PATH = Path("data/processed/lgbm_ready/train.csv")
+TARGET_DATA_PATH = Path("data/processed/lgbm_ready/target.csv")
+METRICS_PATH = Path("metrics/lgbm_metrics.csv")
+PREDICTIONS_PATH = Path("metrics/lgbm_predictions.csv")
+
+INFERENCE_PATH = Path("data/processed/lgbm_ready/inference/inference_train.csv")
+VALIDATION_PATH = Path("data/processed/lgbm_ready/inference/inference_target.csv")
+
+# ---------------------------------------------------------
+# Base single-target LGBM
+# ---------------------------------------------------------
+base_lgbm = LGBMRegressor(
+    n_estimators=500,
+    learning_rate=0.05,
+    max_depth=-1,
+    subsample=0.8,
+    colsample_bytree=0.8,
+    random_state=42,
+    n_jobs=-1,
+    verbose=0,
+)
+
+model = MultiOutputRegressor(base_lgbm)
+
+
+def build_features(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    For LGBM multi-output here, each row is:
+    id | t1 | t2 | ... | tN
+
+    We drop 'id' and use the time columns as features/targets.
+    """
+    return df.drop(columns=["id"])
+
+
+# ---------------------------------------------------------
+# Load data
+# ---------------------------------------------------------
+train_data = pd.read_csv(TRAIN_DATA_PATH)
+target_data = pd.read_csv(TARGET_DATA_PATH)
+
+inference_data = pd.read_csv(INFERENCE_PATH)
+validation_data = pd.read_csv(VALIDATION_PATH)
+
+# Sanity
+# print(train_data.head())
+# print(target_data.head())
+# print(inference_data.head())
+# print(validation_data.head())
+
+train_df = build_features(train_data)
+target_df = build_features(target_data)
+inference_df = build_features(inference_data)
+validation_df = build_features(validation_data)
+
+# ---------------------------------------------------------
+# Fit model
+# ---------------------------------------------------------
+model.fit(train_df, target_df)
+
+# Predict for inference windows
+preds = model.predict(inference_df)
+# If you want integer-unit forecasts, uncomment:
+# preds = np.round(preds)
+
+# ---------------------------------------------------------
+# Build long-format prediction DataFrame
+# ---------------------------------------------------------
+preds_df = pd.DataFrame(preds)  # shape: [n_rows, HORIZON]
+
+# Long form: one row per (row_index, horizon_step)
+preds_df_long = preds_df.stack().reset_index()
+preds_df_long.columns = ["id_index", "h_raw", "forecast"]
+
+# Map back to actual ids using validation_data row order
+preds_df_long["id"] = validation_data.iloc[preds_df_long["id_index"]]["id"].values
+preds_df_long["h"] = preds_df_long["h_raw"].astype(int) + 1  # 1..HORIZON
+preds_df_long["model"] = "lightgbm"
+
+preds_df_final = preds_df_long[["id", "model", "h", "forecast"]]
+print(preds_df_final.head(HORIZON))
+
+# ---------------------------------------------------------
+# Build long-format truth for validation horizon
+# ---------------------------------------------------------
+long = validation_data.melt(
+    id_vars=["id"],
+    var_name="h",
+    value_name="sales",
+)
+long["h"] = long["h"].astype(int)
+
+# ---------------------------------------------------------
+# Compute metrics per id
+# ---------------------------------------------------------
+metrics_rows = []
+for (sku_id, model_name), g_fore in preds_df_final.groupby(["id", "model"]):
+    g_test = long[long["id"] == sku_id].copy()
+    if g_test["h"].max() < HORIZON:
+        # safety check – skip if somehow shorter
+        continue
+
+    merged = pd.merge(
+        g_test[["id", "h", "sales"]],
+        g_fore[["id", "h", "forecast"]],
+        on=["id", "h"],
+        how="inner",
+    )
+    if merged.empty:
+        continue
+
+    y_true = merged["sales"].values
+    y_pred = merged["forecast"].values
+
+    m = mae(y_true, y_pred)
+    b = bias(y_true, y_pred)
+    s = m + abs(b)
+
+    metrics_rows.append(
+        {
+            "id": sku_id,
+            "model": model_name,
+            "mae": float(m),
+            "bias": float(b),
+            "score": float(s),
+        }
+    )
+
+metrics_df = pd.DataFrame(metrics_rows)
+metrics_df.to_csv(METRICS_PATH, index=False)
+preds_df_final.to_csv(PREDICTIONS_PATH, index=False)
+
+print("Saved:")
+print(f" - forecasts → {PREDICTIONS_PATH}")
+print(f" - metrics   → {METRICS_PATH}")

models/model_selection_audit.py

@@ -0,0 +1,28 @@
+import pandas as pd
+
+# Load winner table
+ledger = pd.read_csv("metrics/best_by_sku.csv")  # IF already saved, else skip this line
+
+# Load full metrics output (each model x sku)
+metrics = pd.read_csv("metrics/combined_metrics.csv")
+
+
+# Step 3: Rank models (in case needed later)
+ledger["model_rank"] = ledger.groupby("id")["score"].rank(method="first")
+
+# Step 4: Rename columns as required
+ledger.rename(columns={
+    "id": "sku",
+    "model": "best_model"
+}, inplace=True)
+
+# Optional note column placeholder
+ledger["note"] = ""
+
+# Step 5: Export
+ledger = ledger[["sku", "regime", "best_model", "mae", "bias", "score", "model_rank", "note"]]
+
+ledger.to_csv("metrics/model_selection_audit.csv", index=False)
+
+print("Ledger created at metrics/model_selection_audit.csv")
+ledger.head()

models/select_best_models.py

@@ -0,0 +1,31 @@
+import pandas as pd
+from pathlib import Path
+
+METRICS_PATH = Path("metrics/combined_metrics.csv")
+BEST_PATH = Path("metrics/best_models.csv")
+BEST_MODEL_OVERALL_PATH = Path("metrics/best_model_overall.csv")
+
+
+def main():
+    df = pd.read_csv(METRICS_PATH)
+
+    # Identify best model per SKU by minimum score
+    best = (
+        df.sort_values(["id", "score"])
+          .groupby("id")
+          .head(1)
+          .reset_index(drop=True)
+    )
+
+    best = best.rename(columns={
+        "model": "best_model"
+    })
+
+    best.to_csv(BEST_PATH, index=False)
+    print(best["best_model"].value_counts(normalize=True).round(2))
+    print(f"Saved best models to {BEST_PATH}")
+    df.groupby("model")["score"].mean().sort_values().to_csv(BEST_MODEL_OVERALL_PATH)
+
+
+if __name__ == "__main__":
+    main()

policies.py

@@ -0,0 +1,36 @@
+# Example override and freezes
+# OVERRIDE_THRESHOLD_BY_REGIME = {
+#     "High-High": 0.30,  # 30%
+#     "Low-High":  0.25,  # 25%
+#     "Low-Low":   0.20,  # 20%
+# }
+
+# FREEZE_HORIZON_BY_REGIME = {
+#     "High-High": 14,  # minimal stabilizing window
+#     "Low-High":   7,
+#     "Low-Low":   28,
+# }
+
+MODEL_PRIORITY_BY_REGIME = {
+    "High-High": [
+        "lightgbm",
+        "SeasonalExponentialSmoothingOptimized",
+        "AutoARIMA",
+    ],
+    "Low-High": [
+        "lightgbm",
+        "HoltWinters",
+        "SeasonalExponentialSmoothingOptimized",
+    ],
+    "Low-Low": [
+        "HistoricAverage",
+        "lightgbm",
+        "SimpleExponentialSmoothingOptimized",
+    ],
+    # Optional explicit intermittent cluster if you later tag it
+    "Intermittent": [
+        "CrostonSBA",
+        "CrostonOptimized",
+        "HistoricAverage",
+    ],
+}

requirements.txt

@@ -0,0 +1,7 @@
+streamlit
+pandas
+numpy
+plotly
+lightgbm
+scikit-learn
+chronos-forecasting==2.2.0

run_pipeline.py

@@ -0,0 +1,75 @@
+#!/usr/bin/env python
+"""
+Forecast Sandbox v1.0
+End-to-end pipeline runner.
+
+Runs the full sequence:
+
+1. python -m data.prepare_data
+2. python -m data.prepare_data_lgbm
+3. python -m models.compute_baselines
+4. python -m models.lgbm_modeling
+5. python -m models.combine_metrics
+6. python -m models.select_best_models
+7. python -m models.generate_first_insights
+8. python -m models.model_selection_audit
+
+## SCRIPT EXECUTION DEPENDENCIES GRAPH
+
+```
+prepare_data ──▶ prepare_data_lgbm ──▶ lgbm_modeling
+              └─▶ compute_baselines
+compute_baselines + lgbm_modeling ──▶ combine_metrics
+combine_metrics ──▶ select_best_model
+select_best_model ──▶ generate_first_insights
+generate_first_insights ──▶ model_selection_audit
+```
+
+"""
+
+import subprocess
+import sys
+from datetime import datetime
+
+STEPS = [
+    # ("Prepare FreshNet-50K-derived train/test data", ["python", "-m", "data.prepare_data_freshnet"]),
+    # ("Prepare LightGBM training dataset", ["python", "-m", "data.prepare_data_lgbm_fresh"]),
+    # ("Compute statistical baselines", ["python", "-m", "models.compute_baselines"]),
+    # ("Train LightGBM model", ["python", "-m", "models.lgbm_modeling"]),
+    ("Inference on Chronos2 model", ["python", "-m", "models.chronos_inference"]),
+    ("Combine metrics", ["python", "-m", "models.combine_metrics"]),
+    ("Select best model per SKU", ["python", "-m", "models.select_best_models"]),
+    ("Generate first insights & plots", ["python", "-m", "models.generate_first_insights"]),
+    ("Create model selection audit ledger", ["python", "-m", "models.model_selection_audit"]),
+]
+
+
+def run_step(description: str, cmd: list[str]) -> None:
+    print(f"\n[{datetime.now().isoformat(timespec='seconds')}] ▶ {description}")
+    print(" Command:", " ".join(cmd))
+    completed = subprocess.run(cmd)
+    if completed.returncode != 0:
+        print(f"[ERROR] Step failed: {description}", file=sys.stderr)
+        sys.exit(completed.returncode)
+    print(f"[OK] {description}")
+
+
+def main() -> None:
+    print("=== Forecast Sandbox v1.0 — Full Pipeline Run ===")
+    print(f"Started at: {datetime.now().isoformat(timespec='seconds')}\n")
+
+    for description, cmd in STEPS:
+        run_step(description, cmd)
+
+    print(f"\nFinished at: {datetime.now().isoformat(timespec='seconds')}")
+    print("All steps completed successfully.")
+    print("Key outputs:")
+    print("  - metrics/combined_metrics.csv")
+    print("  - metrics/best_by_sku.csv")
+    print("  - metrics/model_selection_audit.csv")
+    print("  - insights/model_score_ranking.png")
+    print("  - insights/regime_model_performance.csv")
+
+
+if __name__ == "__main__":
+    main()

utils/metrics.py

@@ -0,0 +1,37 @@
+import numpy as np 
+
+
+# Adding type hints for better code clarity and numpy style comments for documentation  
+def mae(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+    """Mean Absolute Error
+    Parameters
+    ----------
+    y_true : np.ndarray
+        True values
+    y_pred : np.ndarray
+        Predicted values
+    Returns
+    -------
+    float
+        Mean Absolute Error
+    """
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
+    return np.mean(np.abs(y_true - y_pred))
+
+def bias(y_true: np.ndarray, y_pred: np.ndarray) -> float:
+    """Bias
+    Parameters
+    ----------
+    y_true : np.ndarray
+        True values
+    y_pred : np.ndarray
+        Predicted values
+    Returns
+    -------
+    float
+        Bias
+    """
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
+    return np.mean(y_pred - y_true)