I have store level data which can range from 3 months to 1 year. This data is basically store weekly level data. For a given store I want to cluster stores as similar stores based on any input variable like sales, count of customers etc.
The criterion for saying that a store is similar to another store is that "the magnitude as well as the trend of the two stores should match" i.e. if I want to define two stores as similar stores on "count of customers in a store" then both the stores should have the same magnitude as well as the same trend of number of customers visiting the store weekly.
I tried DTW (dynamic time warping) to segregate the store level data into various clusters, but the problem is that the magnitude of clustered store matches but trend does not.
If I try using correlation to match the trend of the store then the magnitude does not match.
What should be the optimum way so that both magnitude and trend can match, something like this?