GSLAD: Prototype-Regularized Graph Structure Learning for Multivariate Time Series Anomaly Detection
Unsupervised multivariate time series anomaly detection methods typically identify anomalies through forecasting, reconstruction, or representation discrepancies. However, industrial faults may first alter inter-variable structural patterns while individual trajectories remain close to normal, resulting in weak anomaly signals. In this paper, we propose GSLAD, a prototype-regularized graph structure learning framework that uses structural deviations for anomaly scoring. GSLAD adopts a two-phase training strategy. First, a condition-aware graph learner and a graph-based forecaster are optimized with predictive supervision. The inferred normal graphs are then clustered into multiple structu...
| Authors | Zepeng Zhang, Fuad Khuri, Keivan Faghih Niresi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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