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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?

https://stackoverflow.com/questions/758036/predict-next-event-occurrence-based-on-past-occurrences

https://stackoverflow.com/questions/7615294/how-to-predict-when-next-event-occurs-based-on-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.

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