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I am using multi linear regression to do sales quantity forecasting in retail. Due to practical issues, I cannot use use ARIMA or Neural Networks.

I split the historical data into train and validation sets. Using a walk forward validation method would be computationally quite expensive at this point. I have to take x number of weeks preceding current date as my validation set. The time series prior to x is my training set. The problem I am noting with this method is that accuracy is far higher during the validation period as compared to the future prediction. That is, the further we move from the end of the training period, the less accurate the prediction / forecast. How best can I control this problem?

Perhaps a smaller validation period, will allow the training period to come closer to the current date and hence provide a more accurate forecast; but this hurts the value of validation.

Another thought is to cheat and give both the training and validation historical data during training. As I am not using neural nets, the selected algo should not be over-fitted. Please correct me if this assumption is not right.

Any other thoughts or solution would be most welcome.

Thanks

Regards,

Adeel

Adeel Hashmi
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1 Answers1

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If you're not using ARIMA or DNN, how about using rolling windows of regressions to train and test the historical data?

Troy D
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