I am trying to build a weekly demand forecast model using random forest regression. The model will be responsible for producing a forecast for ~3000-5000 different products at 200-300 stores. I am currently in the process of building the input features and came across several products where the demand is very sparse. For certain products, there are several weeks where we have 0 demand.
I have a few questions about handling these situations:
- Is it better to detect these specific cases and handle them with a different technique (rolling average)?
- In regards to the random forest model, is it valuable to include weeks with 0 demand for the model to learn?