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arXiv Machine Learning By Xudong Mou, Tiejun Wang, Rui Wang, Hui Wang, Pin Liu, Tianyu Wo, Xudong Liu, Renyu Yang

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

Read the original on arXiv Machine Learning →

MORA is a drift‑robust time‑series anomaly detection framework that addresses the ambiguity between distribution drift and true anomalies by defining a temporal change disambiguation problem. It reconstructs a local target from paired short‑ and long‑term views, using the reconstruction gap as a measure of contextual support to adjust the primary anomaly score. The method requires no drift annotations or online adaptation and demonstrates strong robustness to non‑stationarity while maintaining sensitivity to genuine anomalies on four TSAD benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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