Ignacio Oguiza

Thanks for sharing your thoughts @vrodriguezf! My rationale is that is we are preparing a dataset with a window length of 30 but we only have 20 timesteps for a...

>Anyway, for the first case you propose (100 time steps), there is no need to discard, just padding, right? It depends on whether a minimum unpadded length is established or...

cc: @tkharrat, @vrodriguezf I'd like to update the status of this enhancement request. I believe all the different components required to perform this type of task are in place now....

> Wow, amazing work Ignacio!!! This is a huge addition to the library. I think so. I've started to use categorical embeddings on some datasets, and it works pretty well....

I agree it'd be a great addition. Unfortunately, I won't certainly have time to work on it in the foreseeable future. So unless anybody is interested...

In the error, it says that you are calling a module in this line: ```python learn = ts_learner(unlabeled_dls, InceptionTimePlus, cbs=[MVP(fname=f'{dsid}', window_size=(.5, 1))]) # trained on variable window size ``` There...

I'll close this issue due to the lack of response.

Hi @TKassis, Thanks for creating this feature request. I've taken a look at the paper and I think it's great! I'd love to add this functionality to `tsai` asap. (I've...

Hi @TKassis, > Yes, I can help with that. I'll put together an example notebook. Wow, that'd be great! Thanks a lot for that!! Please, let me know how can...

Hi, I just want to provide an update on this long-standing issue (enhancement). I've reviewed the code @TKassis proposed and have found a few issues with it. I've then refactored...