כתבה
arXiv cs.LG ·
MORA: חידוש בדטקטיביות של תקלות בזמן-רצף
MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection
MORA היא פלטפורמה חדשה לדטקטיביות של תקלות בזמן-רצף, המשתמשת במודלי LangGraph ו-Gemini.
תקציר מקורי באנגליתarXiv:2610.09473v1 Announce Type: new 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 fo
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