Patch-Wise-Based Self-Supervised Learning for Anomaly Detection on Multivariate Time Series Data

Multivariate time series anomaly detection is a crucial technology to prevent unexpected errors from causing critical impacts. Effective anomaly detection in such data requires accurately capturing temporal patterns and ensuring the availability of adequate data. This study proposes a patch-wise fra...

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Autori principali:	Seungmin Oh, Le Hoang Anh, Dang Thanh Vu, Gwang Hyun Yu, Minsoo Hahn, Jinsul Kim
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2024-12-01
Serie:	Mathematics
Soggetti:	time series anomaly detection multivariate time series patch-wise learning pre-trained model self-supervised learning channel dependencies
Accesso online:	https://www.mdpi.com/2227-7390/12/24/3969
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