I am working on a fleet management system these days: basically, I am trying to predict the usage ‘y’ of our fleet in a zip code in the future. We have some factors ‘X’, such as number of active users, number of active merchants etc.
If I can fix the time horizon, the problem will become relatively easy:
y = beta * X
However, there is no ‘golden’ time horizon. At any moment, I need to make a call whether I need to send more (or less) cars to that zip code.
Event worse, not all my factors are same. Some factors are strong in short term prediction, some factors are strong in long term prediction. If I force to combine them into one single regression model, I am afraid that I will be hurting the overall regression performance.
I was thinking about multivariate regression, but it does not really solve the problem. Multivariate regression might just give me an ‘average (not in a statistical sense)’ model that tries to predict multiple time-horizon, but due to each factor’s unique predicting power, it might not predict well in any time horizon.
I’d recommend fitting a multilevel model in Stan—hey, I’d even do it myself if you paid me enough! Maybe commenters have some more specific ideas.