Offical implementation of "Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series" (ICLR 2022). [paper]
torch==1.7.1
./models: This directory includes the code of GANF as well as basline methods../checkpoint: This directory stores the trained models. The trained models for the datasets SWaT and Metr-LA are given in./checkpoint/eval../train_water.pyand./train_traffic.py: These programs are used to train GANF on the corresponding datasets../data: This directory is used to store the datasets.
The paper uses three datasets for experiments:
- SWaT: This water system dataset can be requested from iTrust. We utilze the attack_v0 data in Dec/2015 for experimentation. You may need to first convert the file format to .csv to use our code. Then, use
./dataset.pyto perform train/val/test split. - Metr-LA: This traffic dataset does not include ground-truth outliers. It can be used for exploratory studies of density estimation. The dataset can be downloaded from this GitHub.
- PMU: This power grid dataset is proprietary and we are unable to offer it for public use.
To train a GANF model on SWaT, run the bash script:
bash train_water.sh
The training log will be located at ./log as a reference to reproduce the results in the paper.
We also provide trained models in ./checkpoint/eval for evaluation. You can call:
python eval_water.py
To train a GANF model on Metr-LA, run:
python train_traffic.py
If you find this repo useful, please cite the paper. Thank you!
@inproceedings{
dai2022graphaugmented,
title={Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series},
author={Enyan Dai and Jie Chen},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=45L_dgP48Vd}
}