I am working on a real life problem of forecasting 3 days sales for a retail store. I am thinking of applying a hybrid model (ARIMAX+Neural network) i.e. Dynamic regression with regressors using auto.arima, then fitting a neural network model on the residuals. The final forecast will be $y=L+N$ where $L=$ forecast from ARIMAX and $N=$ forecast of residuals from NNETAR. Has anyone tried this approach?

Also, I need help in applying the cross validation techniques. I have daily sales data from Jan14-June17. Would it be worth to tune the parameters using cross validation techniques(Adding months/quarters) or should I go ahead training the model only once (let’s say from Jan14-Dec16) and measure the accuracy on the rest? (Test & Validation)? What could be the best approach given the fact that I need only 3 days forecasts?


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