Comments (3)
Also, it would be great to know how much RevIN benefits the patch Transformer. Thank you!
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Hi @jerrywn121 , thanks for your questions! We use residual attention because we want to be as close as possible to the original Transformer model in NLP, and only find the key factors that make it work well when applying it to time series datasets (patch; channel-independence). Also, residual attention was used in some previous papers like TST (https://arxiv.org/abs/2010.02803). We haven't studied the effect of it yet.
For RevIN, we do the additional ablation in table 11 in the paper. It improves the model marginally in general. Hope this helps.
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Got it. Thank you for your reply!
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Related Issues (20)
- Multivariate Time Series Classification HOT 6
- about res attention
- how to explain the performance discrepancy in different model? HOT 1
- Error while running finetune.py
- Changing to channel Mixing HOT 3
- lradj的问题
- Operations on reverse normalization
- On the question of the number of channels
- Question about exogenous variables HOT 1
- Issues related to training models in a self-supervised learning approach and forecasting using alas models
- Question about batch_size, patch_len and stride HOT 1
- Question about Revin
- How to use time_feature
- question about how to run MS task
- Obtain the MSE of each variable when i do the "M" prediction
- 请问用到了GPU加速吗 HOT 5
- how to use learner.distributed(), in self supervised pretrain code ?
- How does the visualization of Attention Weights organize the code? HOT 2
- scale
- RevIN and StandardScaler HOT 15
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