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I have a question pertains to time-series analysis.

For example, I have a time-series data about the downloads of an app for a month. And I want to know that the effects on the downloads time-series in terms of a) an app being added into a recommendation list and b) the change of the ranking of the app on the list.

I can give an example of the data I have:

So, an app X has its overall downloads time-series: 7/1/2020: 100k, 7/2/2020: 120k, 7/3/2020: 150k, .... 8/1/2020: 3k

Also, I have the app's ranking on several recommendation lists, so assume that it has been added and then removed from 3 lists, and I have the ranking history of them:

X was added to List A on 07/08/2020 and removed from the list A on 07/10/2020, and I have the data like this: 07/08/2020: 20, 07/09/2020: 10, 07/10/2020: 2

Likewise, I also have the data from List B and List C

07/02/2020: 3, 07/03/2020: 1, ... 07/25/2020:50

07/01/2020: 30, 07/03/2020: 10, ... 07/29/2020:90

In addition, I have the information about the List ABC,

List A can cover the population of 2 M users, List B with 1 M and List C with 0.2 M

Now I want to know the effects of being added to list A or B or C and the effects of the change of ranking on the downloads time-series.

Is there any good tools in statistics, data mining, or machine learning could help understand this kind of question?

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  • $\begingroup$ Maybe look into intervention analysis. Search this site! $\endgroup$ – kjetil b halvorsen Aug 9 at 15:34

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