This is a very general problem faced by different types of companies. Predict future customer behavior over time.
Imagine that we have 1 million customers with their own resources over time, forming a time series. The classic techniques (statistics with ARIMA filters, Bayesian approach as FBprophet or purely computational as LSTM) here is not very suitable, as I would consider that each series is reasonably independent, many series are super sparse or have just started and treated them individually who train a model for each series. So that if we run individual forecasts it would be very bad.
The most conservative approach asks a first classifier to understand what the time series is like (like a grouper species), so we have curves with the same seasonality and trends and then normalize so that the level is the same. We train the models for each of these groups and then go back to the old level and have an individual forecast.
I would like to know if there is a more suitable technique or a canonical solution for the model to understand and predict all series more accurately, using "knowledge of the group" (of the time series that are more similar) in a more scientific and scalable way.