I am trying to create a model to predict the units that will be sold for different grocery items say in the next week.
I am structuring the problem in a three-step procedure.
- Group together the selling data from a macro-category (e.g. if I want to predict "egg box XYZ" I will consider all the items from category "eggs") summing the selling data from all items of that category for each day. Then I use prophet to predict the selling data for next week for the whole category. I am doing this step as the "whole category" data have more history and should be more robust compared to single items data.
- Implement a prophet model on the specific items. I expect the error to be higher since the data are less consistent.
- Implement a regressor (could be GradientBoost, RandomForest etc.) on the single item using as input variable for a specific day: the previous week selling data for that item, the forecasted sold units for the whole category, the forecasted units (again with prophet) for the specific item, whether we are in a particular time of the year (e.g. boxing day), promotions applied etc. The idea behind this regressor would be to dampen the error of the second step. However I cannot lower the error from the second step.
How could I approach this problem?