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Why not something like this : ``` training_cutoff = 80 validation_cutoff = 90 training = TimeSeriesDataSet( data[lambda x: x.time_idx

For which dataset ? It look like your dataset does not provide any sequences satisfying the minimum prediction length and the minimum encoder length. Are you sure your test set...

I have not checked through break points but I know by experience that you will encounter an error in case some of your categorical classes are absent from the training...

Indeed, I might be wrong but looking at this: https://github.com/jdb78/pytorch-forecasting/blob/308ea850b82d3a8e8a397f58d589dae9da904eff/pytorch_forecasting/data/timeseries.py#L813-L827 It seems that in case self.scalers is already set, scalers are not fitted anymore, except if check_is_fitted() trigger an error.

@Sharaddition , I am regularly using this technique with a previous version of pytorch-forecasting. I have just sync my forks with recent commits. I will create some tests to make...

In such case, I guess you have to make sure your network does not use encoderNormalizer, right ?