This is a PyTorch implementation of the paper: Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection (TNNLS).
The model is implemented using Python3.7 with dependencies specified in requirements.txt
Follow the instruction in https://itrust.sutd.edu.sg/ to download SWaT and WADI datasets. Following Deng & Hooi (2021), we pre-process and downsample the original dataset. See the jupyter notebook in generate_data folder.
Download the SMD dataset from https://github.com/zhhlee/InterFusion. Pre-process and place it to the data folder. Refer to generate_data foler.
- SWaT
python run.py --data data/swat --expid swat --delays [0,6,30,60,120,180,360] --num_nodes 51 --subgraph_size 15 --normalization_window 28000 --pca_compo 5
- WADI
python run.py --data data/wadi --expid wadi --delays [0,6,30,60,120,180,360] --num_nodes 127 --subgraph_size 30 --normalization_window 200 --pca_compo 100
- SMD
Few examples from server machine of different groups. For more information about dataset grouping please refer to Su et al. (2019).
Machine-1-1
python run.py --data data/machine-1-1 --expid machine-1-1 --delays [0,1,5,10,20,30,60] --num_nodes 38 --subgraph_size 10 --pca_compo 8
Machine-2-7
python run.py --data data/machine-2-7 --expid machine-2-7 --delays [0,1,5,10,20,30,60] --num_nodes 38 --subgraph_size 10 --pca_compo 8
Machine-3-4
python run.py --data data/machine-3-4 --expid machine-3-4 --delays [0,1,5,10,20,30,60] --num_nodes 38 --subgraph_size 10 --pca_compo 26
If you find this research useful, please cite our paper:
@article{zheng2023correlationaware,
title={Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection},
author={Yu Zheng and Huan Yee Koh and Ming Jin and Lianhua Chi and Khoa T. Phan and Shirui Pan and Yi-Ping Phoebe Chen and Wei Xiang},
year={2023},
journal={IEEE Transactions on Neural Networks and Learning Systems}
}
