Demand forecasting example and usage of TimeSeriesDataSet for Validation and Testing

[ML-IEE]

• PyTorch-Forecasting version: 0.9.0
• PyTorch version: 1.9.0
• Python version: 3.7
• Operating System: Windows and Linux

Hello,

thanks for the amazing package, I really enjoy your work.

I had a small question regarding the TimeSeriesDataSet and it's relation with training, validation and testing data. For my elaboration, I primarily focus on your example of the Demand forecasting.

If I see it correctly, you are defining for each of the three datasets an individual TimeSeriesDataSet, thus also create a Normalizer for each dataset seperately. However, if I am not mistaken, this would include future information in the normalization process, which is highly problematic in the time series context. On the other hand, I also saw that you designed a specific function for this problem, i.e. the TimeSeriesDataSet.from_dataset().

Therefore my question, if you could elaborate, why you chose the seperate creation of the TimeSeriesDataSet instead of transfering the training Normalizers ?

Thanks in advance

[cserpell]

I have the same question asked in #1118 .