Here is a simple example of a self-learning algorithm that can estimate the next data point in a series with 95% probability:
initialize weights with NAME_1 values

for each data point in the series:
  calculate the predicted value using the current weights
  calculate the error between the predicted value and the actual value
  update the weights using a learning rule, such as gradient descent
  
  if the error is within the desired threshold (e.g. 5%):
    predict the next data point using the current weights
    if the prediction is within the desired threshold (e.g. 95%):
      return the prediction
  end if
end for

return the prediction

This algorithm uses a simple linear model to predict the next data point, and updates the weights using gradient descent to minimize the 

error between the predicted value and the actual value. The weights are initialized with NAME_1 values, and the algorithm continues to 

learn and refine the predictions as it processes more data.

It's important to note that this is a very simple example and it may not be suitable for real-world applications where the data is more 

complex and the desired threshold is not as clear. Also, the learning rate and the number of iterations should be carefully chosen.




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could you make an example with this method in quickbasic?
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