https://github.com/manigalati/usad

https://github.com/manigalati/usad

Last synced: about 2 months ago
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• Host: GitHub
• URL: https://github.com/manigalati/usad
• Owner: manigalati
• License: other
• Created: 2020-08-07T12:50:17.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2024-03-18T09:04:09.000Z (6 months ago)
• Last Synced: 2024-06-14T01:26:26.152Z (3 months ago)
• Language: Jupyter Notebook
• Size: 2.74 MB
• Stars: 191
• Watchers: 6
• Forks: 57
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# USAD - UnSupervised Anomaly Detection on multivariate time series

Scripts and utility programs for implementing the USAD architecture.

Implementation by: Francesco Galati.

Additional contributions: Julien Audibert, Maria A. Zuluaga.

## How to cite

If you use this software, please cite the following paper as appropriate:

    Audibert, J., Michiardi, P., Guyard, F., Marti, S., Zuluaga, M. A. (2020).

    USAD : UnSupervised Anomaly Detection on multivariate time series.

    Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, August 23-27, 2020

## Requirements

 * PyTorch 1.6.0

 * CUDA 10.1 (to allow use of GPU, not compulsory)

## Running the Software

All the python classes and functions strictly needed to implement the USAD architecture can be found in `usad.py`.

An example of an application deployed with the [SWaT dataset] is included in `USAD.ipynb`.

## Copyright and licensing

Copyright 2020 Eurecom.

This software is released under the BSD-3 license. Please see the license file_ for details.

## Publication

Audibert et al. [USAD : UnSupervised Anomaly Detection on multivariate time series]. 2020

[SWaT dataset]: https://itrust.sutd.edu.sg/itrust-labs_datasets/dataset_info/#swat

[USAD : UnSupervised Anomaly Detection on multivariate time series]: https://dl.acm.org/doi/pdf/10.1145/3394486.3403392