I'm not sure what words I should look for. I have an under determined dataset of 8000 correlated variables (sales) over 12 months (ie 12 observations for each variable). And I basically want to predict the future. Where should I start? PCA?
My question is, what are the techniques used to deal with lots of (correlated) variables and few observations in the case of time series.
I'm looking for orientations (but a full solution is of course welcome!).
I'm agnostic, so stats/econometrics/model-based are as fine as machine learning/AI/not-model-based, as long as they yield results that are useful and mean something. Repeatable/standardizable solutions are more than welcome.