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0 pointsdisgruntledphd22y ago0 comments

if the variables in X(t) have the same time steps, I'd probably look at the cross correlation function of the X vs y, and then built another model on the X to predict X(t+n) and use that as an input for Y(t).

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hackerlight2y ago· 1 in thread

> 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?

disgruntledphd2OP2y 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.

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