Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2509.17235v1
• Date: Sun, 21 Sep 2025 21:00:21 GMT
• Title: Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection
• Authors: Jiazhen Chen, Mingbin Feng, Tony S. Wirjanto,
• Abstract summary: Anomaly detection in high-dimensional time series data is pivotal for numerous industrial applications.<n>We propose the Prospective Multi-Graph Cohesion (PMGC) framework for multivariate TSAD.<n>PMGC exploits spatial correlations by integrating a long-term static graph with a series of short-term instance-wise dynamic graphs.
• Score: 5.89889361990138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Anomaly detection in high-dimensional time series data is pivotal for numerous industrial applications. Recent advances in multivariate time series anomaly detection (TSAD) have increasingly leveraged graph structures to model inter-variable relationships, typically employing Graph Neural Networks (GNNs). Despite their promising results, existing methods often rely on a single graph representation, which are insufficient for capturing the complex, diverse relationships inherent in multivariate time series. To address this, we propose the Prospective Multi-Graph Cohesion (PMGC) framework for multivariate TSAD. PMGC exploits spatial correlations by integrating a long-term static graph with a series of short-term instance-wise dynamic graphs, regulated through a graph cohesion loss function. Our theoretical analysis shows that this loss function promotes diversity among dynamic graphs while aligning them with the stable long-term relationships encapsulated by the static graph. Additionally, we introduce a "prospective graphing" strategy to mitigate the limitations of traditional forecasting-based TSAD methods, which often struggle with unpredictable future variations. This strategy allows the model to accurately reflect concurrent inter-series relationships under normal conditions, thereby enhancing anomaly detection efficacy. Empirical evaluations on real-world datasets demonstrate the superior performance of our method compared to existing TSAD techniques.

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