Say you had a set of users, tens of thousands. You have time series of each of their behaviors using this app. How might you use these time series to predict future behavior of new users?
The intuitive solution is to feature engineer behavior of users and use that to train a model, such as average weekly minutes in app and things like that. My issue with this approach is that you lose a great deal of information.
What I'm wondering is if there's a better technique for aggregating a large set of time series data to build a predictive model. Perhaps an LSTM would work but that seems like it wouldn't capture the nuances of the dataset and I don't believe they're typically used to aggregate predictions on a variety of individual samples.