I have many SKUs/products that have missing historical values.
By missing, it means it has no data or order at all. I'm tempted to say intermittent but there are not regularly intermittent to make use of method like Croston model.
Also it's not helpful that we don't have an active indicator to indicate whether this product is still alive or not.
I'm thinking of transforming this very sparse demand data into a cumulative sum. For example, for the following sparse demand data
raw <- c(13,45,0,0,0,0,0,0,14,20,0,0,0,13,0,0,0)
cumsum <- c(13,58,58,58,58,58,58,72,92,92,92,92,105,105,105,105)
And then apply forecasting methods on the transformed cumulative sum instead.
We do have other methods applied like top-bottom approach, clustering with other similar SKUs, etc. But for this exercise, I want to explore this transformation approach.
Is there any literature that speaks about this transformation for forecasting? What are the models being developed specifically for this transformed data if any?