solderzzc/Anomaly-Transformer

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

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. (Note: Higher PyTorch version may cause error.)
2. Download data. You can obtain four benchmarks from Tsinghua Cloud. 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 ./scripts/SMD.sh
bash ./scripts/MSL.sh
bash ./scripts/SMAP.sh
bash ./scripts/PSM.sh

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 xjh20@mails.tsinghua.edu.cn, whx20@mails.tsinghua.edu.cn.