I have product sales data for which I would like to predict what will be the sales for each product at the product level, product store level, product store and region level etc.
To solve this problem, I was reading up online about this topic and came across some useful basis tutorials for hierarchical forecasting such as Nixtla hierarchical forecast and DARTS package.
However, in real world, there are products which experience has high demand, some products which has sporadic demand, some products which has erratic demand etc. There could be products which has no demand. So, you will see lot of zeros for demand and some non-zeros for such products
So my question is
should we first do "demand classification" using traditional ABC XYZ technique ([here])1 and then use it as one of levels in hierarchical forecasting? Or do forecasting only for products that has lot of data (stable demand etc)
How does hierarchical forecasting work for products with uneven time series, lot of zeros etc.how should we handle them?
Can someone share your views on this?