Choose proper moving average kernel for short input about autoformer HOT 5 CLOSED

Hi, thanks for your usage of this repo.
(1) input length
I think the input length should be re-considered. We have discussed the input length in Appendix C of the paper. Generally speaking, longer inputs will provide more information, which can benefit the forecasting. Also, the input length can be affected by the sampling rate. Thus, I suggest to re-check the data pattern for the determination of input length.

(2) Only use the decoder (If your prediction horizon is long)
if you only have a limitation on the input length, while the forecasting horizon is long, I think you can only adopt the Autoformer decoder. And use the input length as the 'label_len' in this repo.

(3) input is short and output is also short.
In this condition, you can remove the moving average, and only use the Auto-Correlation. Because the shorter time series will contain a simpler temporal pattern, maybe you don't need decomposition.

from autoformer.

Thank you for your respond! It is very insightful.

For (2), could you give some more clarification? If we only use the decoder, then how to handle the encoder's output? Or you are saying that use only decoder to generate all Q, K, V and let decoder be the whole model? Thank you!

from autoformer.

I mean the latter case: "only decoder to generate all Q, K, V and let decoder be the whole model?"
In your case, the encoder seems to be meaningless if it only captures the information of 8 time points. You can adopt the Autoformer decoder to aggregate past information and generate the future. Note that, in this case, the decoder does not have the cross information, thus, it only contains one Auto-Correlation block, which is more like an encoder.

from autoformer.

Thank you very much! That make sense!

from autoformer.

condition

for (3), how to remove the moving average please?
I've tried to remove it from the settings but coming with error.

from autoformer.