Tiejun Wang

MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection

Time-series anomaly detection (TSAD) identifies deviations from patterns learned from historical data. In non-stationary settings, distribution drift and true anomalies can cause similar local changes, making it difficult to tell whether a deviation reflects abnormality or evolving context. Existing methods typically a …