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I have data about sale as example follows:

Date Product Category Sale Promotion of product 1 Promotion of product 2 Promotion of product 3
01/01/2020 Product1 A 3 1 0 0
01/01/2020 Product2 A 4 0 1 0
01/01/2020 Product3 B 2 0 0 1
02/01/2020 Product1 A 7 1 0 0
02/01/2020 Product2 A 4 0 1 0
03/01/2020 Product1 A 2 0 0 0
03/01/2020 Product2 A 1 0 0 0
03/01/2020 Product3 B 9 0 0 1

Suppose I have 50 products and because of the hypothesis that the sale and promotion of one product can impact to others. So, I would like to forecast each product in parallelly. (I mean that model forecast whose the outputs are the sale of each product) What are the methods to solve this problem?

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  • $\begingroup$ How mNY DIFFERENT PRODUCTS? Maybe look at hierarchcal forecasting $\endgroup$
    – kjetil b halvorsen ♦
    Commented Oct 1, 2021 at 7:46
  • $\begingroup$ Yes! There are more than 50 products for each category. I also consider about Hierarchical forecasting, but i dont know what is the package which can be used for this methods in python with exogenous variable. $\endgroup$
    – Sherry
    Commented Oct 1, 2021 at 8:03

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Similar questions have been asked and given fairly comprehensive answers here and here.

A few comments:

  • The main keywords you will want to look for are i) 'Hierarchical Modeling' and ii) 'Panel Modeling'.

    • Panel modeling is concerned with modeling multiple time series at once.
    • Hierarchical modeling makes use that some products belong to the same category.

  • If you have a large number of products, it may make sense to engineer the promotional features. For example, instead of having 1 column per product, maybe try columns like:

    1. Is this product on Sale?
    2. How many other products are on sale?
    3. Are products from the same category on sale?
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