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

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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Bibliographic Details
Main Authors: Seungmin Oh, Le Hoang Anh, Dang Thanh Vu, Gwang Hyun Yu, Minsoo Hahn, Jinsul Kim
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Mathematics
Subjects:
time series anomaly detection
multivariate time series
patch-wise learning
pre-trained model
self-supervised learning
channel dependencies
Online Access:https://www.mdpi.com/2227-7390/12/24/3969
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