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

Abstract: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 adapt to detected shifts or learn drift-insensitive representations, but do not resolve this ambiguity. We define this problem as \emph{temporal change disambiguation}: determining whether a local deviation is explained by broader temporal evolution. We introduce MORA, a drift-robust TSAD framework that reconstructs the same local target from paired short- and long-term views. The reconstruction gap measures contextual support for a local deviation, and a data-dependent correction mechanism conservatively adjusts the primary local anomaly score. Context can only reduce the score when it improves reconstruction of the same target. MORA needs neither drift annotations nor online adaptation. Experiments on four TSAD benchmarks show strong robustness to non-stationarity while preserving sensitivity to genuine anomalies.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.09473 [cs.LG]

(or arXiv:2610.09473v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.09473 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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