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arXiv AI By Fan Xu, Hao Wu, Zhi Song, Hanbo Huang, Wenzhuo Ma, Nan Wang, Wei Gong, Xibin Zhao

CLOSURE: Cross-Channel Closure for Multivariate Time Series Anomaly Detection

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CLOSURE introduces a two‑task framework for multivariate time‑series anomaly detection that separately evaluates recoverability and cross‑channel consistency. The recoverability task uses a diffusion reconstructor conditioned on a discrete memory of normal temporal bases, while the consistency task employs a relation‑closure operator that predicts each channel from its peers using sparse signed gates. Experiments demonstrate that combining these two scores outperforms recent reconstruction, frequency, and topology‑based detectors on public benchmarks, highlighting the importance of assessing consistency alongside recoverability.

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 AI.

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