I need to build a model using climate variables (temperature, rainfall) to predict monthly sales (horizon of 6 months) for certain product. The data has strong seasonality and a standard regression model would works fine, the problem is that the historic data will not be updated, meaning that the observed data points will not be incorporated into the model.
Whats a good way to solve this? What if i split the sales data into levels (say 'WEAK', 'NORMAL', 'HIGH', VERY HIGH') and then use a regression tree? Is there any 'danger' in doing this?
For a standard regression model, how i deal with the seasonality if the new points will not be incorporated?
I'm using R, thanks!