GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning
Open accessAbstract
System monitoring and anomaly detection is a crucial task in daily operation. With the rapid development of cyber-physical systems and IT systems, multiple sensors get involved to represent the system state from different perspectives, which inspires us to detect anomalies considering feature dependence relationship among sensors instead of focusing on individual sensor's behavior. In this paper, we propose a novel Graph Relational Learning Network (GReLeN) to detect multivariate time series anomalies from the perspective of between-sensor dependence relationship learning. Variational AutoEncoder (VAE) serves as the overall framework for feature extraction and system representation. Graph Neural Network (GNN) and stochastic graph relational learning strategy are also imposed to capture the between-sensor dependence. Then a composite anomaly metric is established with the learned dependence structure explicitly. The experiments on four real-world datasets show our superiority in detection accuracy, anomaly diagnosis, and model interpretation.
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Sorted by citations
Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects
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“In order to better capture the dependencies between different variables in multivariate time series, GRELEN [82] and VGCRN [83] introduce the graph structure and in VAE.”
MethodSelf-citation
- Authors
- Zhang, W., Zhang, C., & Tsung, F.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- Tsinghua University (CN)
- Venue
- Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
- Published
- 2022-07
- Pages
- 2390-2397
- OpenAlex
- W4285600291
- Cited by
- 99
- References
- 3
- Citations by year
- 2026: 21 · 2025: 36 · 2024: 27 · 2023: 14 · 2022: 1
- FWCI
- 10.77
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- National Natural Science Foundation of China (71901131, 16216119, 71932006)
- Bulgarian National Science Fund (9222014)
- Hong Kong University of Science and Technology
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- multivariate time series anomaly detection, system monitoring, variational autoencoder, graph neural networks, anomaly diagnosis, model interpretability, cyber-physical systems
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