Comments (1)
axeloh
commented on August 22, 2024
Hello,
MTAD-GAT is indeed a model made for multivariate time-series. If the input is univariate, then the feature-oriented GAT module will have no effect. However, the time-oriented GAT module will still serve the purpose of attending to different time-steps.
Recall that even though datasets SMAP and MSL have univariate output, the input is still multivariate, and so both GAT modules will be utilized.
from mtad-gat-pytorch.
Related Issues (20)
- Loss and some slice of the output tensors become NAN HOT 1
- About data cleaning HOT 1
- Problems encountered in calculating anomaly scores on the SMAP dataset HOT 1
- FileNotFoundError: [Errno 2] No such file or directory: 'datasets/ServerMachineDataset/processed\\machine-1-1_train.pkl' HOT 1
- Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same HOT 1
- The reason why use shuffle in time-series data HOT 1
- about gat_layer
- My understanding of mtad _ gat.py does not reflect the ' Multivariate Time-series ' in the title of the paper. HOT 2
- Computational resource HOT 1
- The issue with the dataset HOT 1
- msl and smap dataset preprocess gives error HOT 2
- Pot results on the SMD dataset
- Multiple inconsistent training results HOT 1
- about the example output,please!!! HOT 1
- Embedding vector dimension issue in the paper HOT 1
- about adjust_predicts() ，please！！！ HOT 2
- The setting about feat_gat_embed_dim and time_gat_embed_dim
- Evaluation Code
- Versions of Ptyhon and Pytorch
- Running it on custome dataset with 20 features
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from mtad-gat-pytorch.