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...

وصف كامل

التفاصيل البيبلوغرافية

المؤلفون الرئيسيون:	Seungmin Oh, Le Hoang Anh, Dang Thanh Vu, Gwang Hyun Yu, Minsoo Hahn, Jinsul Kim
التنسيق:	Artigo
اللغة:	Inglês
منشور في:	MDPI AG 2024-12-01
سلاسل:	Mathematics
الموضوعات:	time series anomaly detection multivariate time series patch-wise learning pre-trained model self-supervised learning channel dependencies
الوصول للمادة أونلاين:	https://www.mdpi.com/2227-7390/12/24/3969
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!