*Bounty: 50*

*Bounty: 50*

I am interest in a (multivariate) algorithm to identify relevant regressors (which are itself time series) to forecast a time series of interest. The question is worded in general terms because this algorithm should be applied on different kinds of time series.

With classical data, I would use for example LASSO and use those variables which non-zero coefficients but I am not really sure how to do that in a (general-purpose) time series context. The reason is that here each indicator may be relevant with a different lag. Furthermore it might be important to take a priori unknown seasonality pattern into account (the method should at best work for time series on an hourly level as well as an monthly level).

Similar questions have already been raised here at CrossValidated, for example,

but I did not find a satisfactory answer. I hope it is therefore okay to post a similar question again.

Random Forest has been suggested in this answer. As with LASSO, it is however not clear to me how to optimally apply these methods in a time series context with arbitrary seasonality patterns.

I do not want to use cross-correlation (as suggested in this answer) because I want to take into account the covariance between the regressors.

tsfresh has been suggested in this answer but I do not see how I can get the most relevant features (meaning variables/regressores plus lags) from that package.

Any hints for Python or R libraries are welcome.