Hi all! I have a question. When I forecast I wou...
# neural-forecast
Hi all! I have a question. When I forecast I would like to forecast 30 days ahead. Thus, in last day of August, I want to forecast the whole month of October (thus 30 days). I want to use the cross_validation function to evaluate my results. I tried adjusting the n_windows, step_size and horizon, but I didn't get to the correct result. Any tips on how to implement time gap when using cross_validation function? Thanks a lot in advance!
Hi @Anna-Maria K.! The library does not support his option currently, it will predict the immediate data after the train/validation set. The easiest solution is to increase the horizon to account for the gap. Be careful, however, that changing the
are very different to the
. It seems that if you do not have September's data, you will need to have 1 window,
, with a large horizon.
Thanks a lot for the clarifications!