Thanks for constructing such an excellent tool. I am using it to compare several models including TFT, DeepAR, N-BEATS, LSTM, GRU. Using the same dataset and fixed encoder length, I increase the prediction horizon (i.e. the decoder length) from 1 day to 21 days. I assume the loss should also increase with the increase of prediction horizon, however, the DeepAR did not work in that way. I listed the loss as follows:
Description
Thanks for constructing such an excellent tool. I am using it to compare several models including TFT, DeepAR, N-BEATS, LSTM, GRU. Using the same dataset and fixed encoder length, I increase the prediction horizon (i.e. the decoder length) from 1 day to 21 days. I assume the loss should also increase with the increase of prediction horizon, however, the DeepAR did not work in that way. I listed the loss as follows:
All other models work perfectly but not DeepAR. I also found a previous issue related to this bug: https://github.com/jdb78/pytorch-forecasting/issues/182
From my experiment, I think it has not been solved. Any suggestions on this issue?
Really appreciated!