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.
Install dependences into a virtualenv:
$ python -m venv env
$ source env/bin/activate
(env) $ pip install -r requirements.txtWritten with python version 3.8.11
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.
Once properly configured, a model can be trained via python train.py.
@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}
}
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