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Eunji Jun

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Open access 2026

CHAIN: Long-Context History-Supervised Multivariate Time Series Anomaly Detection

This paper proposes CHAIN (long-Context History-supervised Anomaly detectIoN), a novel framework that explicitly captures long-term historical contexts under anomaly simulation and supervises current-window anomaly detection via masked cross-attention fusion.

Eunji Jun, Junho Yim · 0 citations

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