# Anomaly-Transformer

**Repository Path**: buptybx/Anomaly-Transformer

## Basic Information

- **Project Name**: Anomaly-Transformer
- **Description**: https://github.com/thuml/Anomaly-Transformer
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-05-07
- **Last Updated**: 2024-05-07

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## 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.

<p align="center">
<img src=".\pics\structure.png" height = "350" alt="" align=center />
</p>

## 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.**

<p align="center">
<img src=".\pics\result.png" height = "450" alt="" align=center />
</p>

## 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 wuhx23@mails.tsinghua.edu.cn.