Multivariate Time Series Anomaly Detection Using Working Memory Connections in Bi-Directional Long Short-Term Memory Autoencoder Network

Multivariate Time Series Anomaly Detection Using Working Memory Connections in Bi-Directional Long Short-Term Memory Autoencoder Network

“Normal” events are characterized as data patterns or behaviors that align with expected operational conditions, while “anomalies” are defined as deviations from these patterns, potentially signaling faults, errors, or unexpected system behaviors. The timely and accurate detection of anomalies plays...

书目详细资料
Main Authors:	Xianghua Ding, Jingnan Wang, Yiqi Liu, Uk Jung
格式:	文件
语言:	English
出版:	MDPI AG 2025-03-01
丛编:	Applied Sciences
主题:	anomaly detection long short-term memory time series data autoencoder multivariate time series
在线阅读:	https://www.mdpi.com/2076-3417/15/5/2861

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