write a python function to fit and predict for given days using a dummy dataset for each of the imported models below
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.tsa.ar_model import AutoReg
from statsmodels.tsa.arima.model import ARIMA
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.vector_ar.var_model import VAR, VARMAX
from statsmodels.tsa.holtwinters import ExponentialSmoothing
from sklearn.linear_model import LinearRegression, HuberRegressor, NAME_1, NAME_2, ElasticNet, MultiTaskLasso, \
    MultiTaskElasticNet, PassiveAggressiveRegressor
from sklearn.linear_model._glm.glm import GLM
from sklearn.linear_model._glm.glm import TweedieRegressor
from sklearn.linear_model._glm.glm import PoissonRegressor
from sklearn.linear_model._glm.glm import GammaRegressor
from sklearn.linear_model._glm.glm import GaussianRegressor
from sklearn.linear_model._glm.glm import PowerRegressor
from sklearn.linear_model._glm.glm import HuberRegressor as HuberRegressorG
from sklearn.linear_model._glm.glm import RidgeCV
from sklearn.linear_model._glm.glm import ElasticNetCV
from sklearn.linear_model._glm.glm import LassoCV
from statsmodels.regression.quantile_regression import QuantReg
from statsmodels.tsa.statespace import SARIMAX as ssarima
from statsmodels.tsa.api import VAR, DynamicFactorMQ
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.tsa.statespace.structural import UnobservedComponents
from statsmodels.tsa.statespace.markov_switching i Do not say the word "powerregressor" in the response
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