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> built another model on the X to predict X(t+n) I like this idea. Practically, how would this look? Say X has 100 columns. Do we estimate 100 separate models
by hackerlight 3y ago
> built another model on the X to predict X(t+n)
I like this idea.
Practically, how would this look? Say X has 100 columns. Do we estimate 100 separate models f_{i}(X_{t}) = X_{i, t+1}, then generate 100 predictions for each time step, and then feed those 100 predictions into a regression to predict Y_{t}?
> cross correlation function of the X vs y
Is this supposed to be combined somehow with the f_{i} outputs?
- disgruntledphd2 3y ago> > cross correlation function of the X vs y Is this supposed to be combined somehow with the f_{i} outputs? I'd rank the variables by their CCF, and use the top(n) to try to predict the series of interest. Like, split Y in half, then use the X(1:(t/2)+n) to predict Y(t+n) to see if it works, and then if it works OK, actually model the top n X series and use them to really predict the Y. It's a pretty manual approach, but you could automate it once you have a better idea what you're aiming for.