I am trying to build an efficient forecasting model to predict sales in the future. I managed to obtain a first pretty solid model using a LSTM network. However, it wasn't sensible enough to large occuring variations.
I then decided to process the problem in a different way by fitting a linear regression over the data, which displays a clear trend. The linear regression was accurate at predicting the trend but not the variations. I then fitted an Random Forest which was accurately predicting the variations but wasn't sensible to the trend: the prediction were following a seemingly stable mean along the time but were way under the effective sales.
Is there any way I can adjust the bias of my Random Forest in order for it to take into account the forecasted trend by the Linear regression ? Is there any other method not involving the bias ?
I already tried training the Random Forest using residuals but this method wasn't successful, only lifting the extra trees plot insignificantly.
Thanks in advance !