spencerbraun / anomaly_transformer_pytorch

PyTorch implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy

Anomaly Transformer in PyTorch

This is an implementation of Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy. This paper has been accepted as a Spotlight Paper at ICLR 2022.

Repository currently a work in progress.

Usage

Requirements

Install dependences into a virtualenv:

$ python -m venv env
$ source env/bin/activate
(env) $ pip install -r requirements.txt

Written with python version 3.8.11

Data and Configuration

Custom datasets can be placed in the data/ dir. Edits should be made to the conf/data/default.yaml file to reflect the correct properties of the data. All other configuration hyperparameters can be set in the hydra configs.

Train

Once properly configured, a model can be trained via python train.py.

Citations

@misc{xu2021anomaly,
      title={Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy},
      author={Jiehui Xu and Haixu Wu and Jianmin Wang and Mingsheng Long},
      year={2021},
      eprint={2110.02642},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}