-
Notifications
You must be signed in to change notification settings - Fork 410
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Choose proper moving average kernel for short input #4
Comments
Hi, thanks for your usage of this repo. (2) Only use the decoder (If your prediction horizon is long) (3) input is short and output is also short. |
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! |
I mean the latter case: "only decoder to generate all Q, K, V and let decoder be the whole model?" |
Thank you very much! That make sense! |
for (3), how to remove the moving average please? |
Hello! Thank you for your well-commented code! I'm currently using Autoformer to deal with some data which have very short input length, such as only 8 timestamps. I noticed that the default moving average kernel size in series decomposition part is 25, which maybe too long for the input in this case. I tried some smaller kernel such as 3, 5, 7. But the model turned out to be worse on validation dataset. Do you have any suggestions about adjusting hyper parameters for short input? Any suggestion would be appreciated. Thank you!
The text was updated successfully, but these errors were encountered: