Problem definition: Predict user's next event date, based on previous event occurrences. The aim is to inter-corporate time dependent and time independent features.

Data: +10 year transactional data generated by millions of users. 80% of users have less than 3 events.

Prediction: Next event date

I've gone through many similar questions, the most related ones were the following:

How to predict when the next event occurs, based on times of previous events?



I am still unsure which method I should follow as the prediction should be on user level, but the users behaviour is really dissimilar. Shall I try to cluster the users first, and create different models for each cluster? Or am I better off fitting a distribution pattern for each user? If i use HNN, how can I intercorporate seasonality variables?

One of my biggest headache is how to transform the transactional dataset into a dataset which can be used for modeling. Previously, I've never dealt with a dataset with different entities (users) and also stochastic process.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.