I have training data consisting of a time series of numerical values (e.g. a user activity metric on a website over a period of 100 days). I also have some categorical attributes of the user (demographics, type of browser, location, etc)
What would be a good approach to predict for a new user (say on day 50) the future value of the activity metric, either as a numeric value or as a class (low, medium, high)?
I could think of the following approaches:
A. Perform time series regression or classification separately for most (or all) combinations of the initial categorical features.
B. Extract 4 to 10 different numerical and/or categorical features from time series (e.g. high, low, average, weekend levels, weekend to weekday ratio, etc), then performing a random forrest classification or regression on the entire set of features.
C. Something else?