Comments (2)
Hi @panjiashu, thanks for your great question! Yes, non-uniformity often occurs with with time series and we have not found an efficient solution to handle this scenario with patching yet.
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Hi @panjiashu, thanks for your question! As @namctin suggested, we haven't take this into consideration yet. But if you want to use PatchTST on non-uniform time series, one way is to re-sample it to make it uniform. Based on table 1, this hopefully won't hurt the performance too much.
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Related Issues (20)
- Multivariate Time Series Classification HOT 6
- .
- Error during installation ”Could not find a version that satisfies the requirement numpy==1.21.3“,but the actual version is now 1.26.4
- Performance about self-supervised learning
- Can this model fit unbalanced panel data(more than one individual)?
- 请问论文中的注意力矩阵在代码里怎么输出
- Multivariate predict univariate HOT 2
- Stock Price Forecasting using PatchTST model HOT 4
- Pretrained Models in Huggingface Repository
- Question about not applying inverse_transform HOT 2
- Question about Table 9.
- outputs和batch_y 序列长度问题
- question about attention layer shared weight
- RuntimeError: required rank 4 tensor to use channels_last format
- 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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