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...
Ze-Peng Zhang, F. Khūrī, Keivan Faghih Niresi et al.
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