First of all, I realise that my question is very broad and that it may be hard to answer this question because of it.
Do you have any advice on how to approach a 'problem' where you need to make forecasts/predictions for 2000+ different products? In other words, each product requires a different forecast/prediction. I have 2 years of historical data on week level (i.e. demand per week per product).
I need to do this in a short time period: I have about a week to do this, hence I am looking for ways that I can quickly make relatively good prediction models. Creating a model for each product and inspecting its performance closely, one by one, would be too time-consuming.
I thought of segmenting the products based on the variance, so that I can employ simple models for products that have a low variance. While this is probably not ideal, it would be a quick way to narrow down the number of models I need to create.
It would be greatly appreciated if you have any practical advice for me on approaching this problem.