AI RESEARCH
GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection
arXiv CS.LG
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ArXi:2501.18196v3 Announce Type: replace Unsupervised anomaly detection of multivariate time series is a challenging task, given the requirements of deriving a compact detection criterion without accessing the anomaly points. The existing methods are mainly based on reconstruction error or association divergence, which are both confined to isolated subsequences with limited horizons, hardly promising unified series-level criterion.