Papers › Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
Ailin Deng, Bryan Hooi
Given high-dimensional time series data (e.g., sensor data), how can we detect anomalous events, such as system faults and attacks? More challengingly, how can we do this in a way that captures complex inter-sensor relationships, and detects and explains anomalies which deviate from these relationships? Recently, deep learning approaches have enabled improvements in anomaly detection in high-dimensional datasets; however, existing methods do not explicitly learn the structure of existing relationships between variables, or use them to predict the expected behavior of time series. Our approach combines a structure learning approach with graph neural networks, additionally using attention weights to provide explainability for the detected anomalies. Experiments on two real-world sensor datasets with ground truth anomalies show that our method detects anomalies more accurately than baseline approaches, accurately captures correlations between sensors, and allows users to deduce the root cause of a detected anomaly.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unsupervised Anomaly Detection | SMAP | GDN | AUC | 98.64 | #8 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | GDN | F1 | 85.18 | #8 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | GDN | Precision | 74.80 | #8 of 9 | Archive leaderboard | report |
| Unsupervised Anomaly Detection | SMAP | GDN | Recall | 98.91 | #8 of 9 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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