Research map / Time series / Anomaly detection
Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GDN instead of the strongest baseline | Anomaly detection · SWaT | F1: 0.77 -> 0.81 | Helped |
| Use GDN instead of the strongest baseline | Anomaly detection · WADI | F1: 0.37 -> 0.57 | Helped |
| Add learned graph structure rather than using a complete graph (ablation) | GDN · SWaT | F1: 0.78 -> 0.81 | Helped |
| Add learned graph structure rather than using a complete graph (ablation) | GDN · WADI | F1: 0.51 -> 0.57 | Helped |
| Add sensor embeddings to the attention mechanism (ablation) | GDN · SWaT | F1: 0.76 -> 0.81 | Helped |
| Add sensor embeddings to the attention mechanism (ablation) | GDN · WADI | F1: 0.49 -> 0.57 | Helped |
| Use attention rather than equal weights for neighbors (ablation) | GDN · SWaT | F1: 0.68 -> 0.81 | Helped |
| Use attention rather than equal weights for neighbors (ablation) | GDN · WADI | F1: 0.48 -> 0.57 | Helped |
Closest papers by meaning
- Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection (AAAI 2025 · similarity 0.91)
- Multi-Scale Dynamic Graph Learning for Time Series Anomaly Detection (Student Abstract) (AAAI 2024 · similarity 0.90)
- GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality (AAAI 2025 · similarity 0.89)
- The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark (NeurIPS 2024 · similarity 0.88)
- Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection (NeurIPS 2025 · similarity 0.87)
- Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders (ICLR 2025 · similarity 0.86)
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