Hi, very good questions. I’ll try to answer them as best as possible in the docs. However here already a couple of quick links to keep you moving:
Priyatham10:
Train - Validation - Test split strategy
The little documentation there is you can find here. I’ll try to find time to give a bit more examples.
Priyatham10:
Why season = lookback+horizon is taken as hyper-parameter for nbeats_learner ?
Season is the maximum Period for the SeasonalityBlock. There are more examples in the link. The default setting worked best for my data but it does help to tweak that one.
Priyatham10:
Why the lr_find plot is not getting it’s loss increased rapidly after a certain learning rate(lr) ?
No idea. I could speculate but I have not investigated the matter.
Priyatham10:
Does the learn.fit_flat_cos is different learning strategy compared to fit_one_cycle ?
Originally I also used the fit_one_cycle, but with the succes of Mish with fit_flat_cos i decided to give it a shot. (Imagenette/ Imagewoof Leaderboards) It did better only introduced more depencies that I wasn’t willing to put up with. In the end I removed that part. But I forgot to remove it from the Readme. I’m not sure if it still helps with the relu as activation. Here is a link to official documentation:
https://dev.fast.ai/callback.schedule#Learner.fit_flat_cos