❗DISCLAIMER: this is a fork of Anomaly-Transformer repository adjusted to run FIREMAN datasets.

Setup environment

How to setup python venv to run the code on linux machine:

sudo apt update
sudo apt install software-properties-common
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt-get install python3.6
sudo apt-get install python3.6-venv
mkdir ~/.venvs
python3.6 -m venv ~/.venvs/anomaly-transformer
source ~/.venvs/anomaly-transformer/bin/activate
pip install torch==1.4.0 sklearn pandas pillow

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

Install Python 3.6, PyTorch 1.4.0. (Note: Higher PyTorch version may cause error.)
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.
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
bash ./scripts/LLFault.sh
bash ./scripts/SinglePhaseSag.sh
bash ./scripts/ThreePhaseGridFault.sh
bash ./scripts/ThreePhaseSensorFault.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_}
}

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