Implementation of USAD (UnSupervised Anomaly Detection on multivariate time series) in PyTorch Lightning.
Original implementation by: Francesco Galati. Original code can be found in: USAD.
To start, first download the data.
Data can be found in:
- Normal data: SWaT Dataset Normal
- Attack data: SWaT Dataset Attack
After downloading them put them in data/raw.
dvc exp run
All the parameters (for example epoch size) can be found in params.yaml.
- pytorch 1.9
- dvc
- pytorch-lighting
- python 3.8
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