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My problem context specifically lies in churn modeling, where accounts have account-specific attributes (like industry, number of employees, etc), but also have longitudinal yearly data (product usage data, contract premium costs, etc).

One example question we want to answer is: Given product usage data over time (annually), as well as their account-level characteristics, which accounts will churn the next year?

Are there any classification algorithms that can account for both time-series effects as well as account-level attributes? I feel like if I just run a normal classification algorithm (like RF, logistic regression, etc), then it'll assume every account is unique with its own attributes, without making the relationship that some observations are from the same account, and are related by time.

Also, if such a method exists, an interpretable one (where I can compute feature importance, etc) would be preferable. Thanks in advance!

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A couple of approaches come to mind:

  • One simple way is run classifications for each category of account-specific features. If account-specific features are categorical already, train a classifier for each category. If they are continuous, you could bin them to obtain groups.

  • Another way is to extract features from the time-series for each account, using e.g. tsfresh package, and then classify each account based on all features.

  • A third approach would be to use Cox proportional hazard models.

  • There are also more complicated models that may be applicable like point process models.

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You can also consider adding account, the identifier, as a characteristic, this would be similar to a fixed effect regression.

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