https://github.com/thuml/Anomaly-Transformer

About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_
https://github.com/thuml/Anomaly-Transformer

anomaly-detection deep-learning time-series

Last synced: about 1 month ago
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About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_

• Host: GitHub
• URL: https://github.com/thuml/Anomaly-Transformer
• Owner: thuml
• License: mit
• Created: 2022-04-22T02:25:58.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-12-29T11:36:58.000Z (12 months ago)
• Last Synced: 2024-08-03T09:09:43.450Z (5 months ago)
• Topics: anomaly-detection, deep-learning, time-series
• Language: Python
• Homepage:
• Size: 26 MB
• Stars: 681
• Watchers: 8
• Forks: 179
• Open Issues: 26
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Anomaly-Transformer (ICLR 2022 Spotlight)

Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Unsupervised detection of anomaly points in time series is a challenging problem, which requires the model to learn informative representation and derive a distinguishable criterion. In this paper, we propose the Anomaly Transformer in these three folds:

- An inherent distinguishable criterion as **Association Discrepancy** for detection.

- A new **Anomaly-Attention** mechanism to compute the association discrepancy.

- A **minimax strategy** to amplify the normal-abnormal distinguishability of the association discrepancy.







## Get Started

1. Install Python 3.6, PyTorch >= 1.4.0. 

(Thanks Élise for the contribution in solving the environment. See this [issue](https://github.com/thuml/Anomaly-Transformer/issues/11) for details.)

2. Download data. You can obtain four benchmarks from [Google Cloud](https://drive.google.com/drive/folders/1gisthCoE-RrKJ0j3KPV7xiibhHWT9qRm?usp=sharing). **All the datasets are well pre-processed**. For the SWaT dataset, you can apply for it by following its official tutorial.

3. Train and evaluate. We provide the experiment scripts of all benchmarks under the folder `./scripts`. You can reproduce the experiment results as follows:

```bash

bash ./scripts/SMD.sh

bash ./scripts/MSL.sh

bash ./scripts/SMAP.sh

bash ./scripts/PSM.sh

```

Especially, we use the adjustment operation proposed by [Xu et al, 2018](https://arxiv.org/pdf/1802.03903.pdf) for model evaluation. If you have questions about this, please see this [issue](https://github.com/thuml/Anomaly-Transformer/issues/14) or email us.

## Main Result

We compare our model with 15 baselines, including THOC, InterFusion, etc. **Generally,  Anomaly-Transformer achieves SOTA.**







## Citation

If you find this repo useful, please cite our paper. 

```

@inproceedings{

xu2022anomaly,

title={Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy},

author={Jiehui Xu and Haixu Wu and Jianmin Wang and Mingsheng Long},

booktitle={International Conference on Learning Representations},

year={2022},

url={https://openreview.net/forum?id=LzQQ89U1qm_}

}

```

## Contact

If you have any question, please contact [email protected].