Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals

Nowadays, multi-sensor technologies are applied in many fields, e.g., Health Care (HC), Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can generate a substantial amount of multivariate time-series data. Unsupervised anomaly detection on multi-sensor time-series...

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Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 35; H. 2; S. 2118 - 2132
Hauptverfasser:	Zhang, Yuxin, Chen, Yiqiang, Wang, Jindong, Pan, Zhiwen
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.02.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Anomalies Anomaly detection attention based BiLSTM Autoregressive models Autoregressive processes CAD Computational modeling Computer aided design Control systems convolutional autoencoder Data models Deep learning Forecasting Human activity recognition Image reconstruction Industrial electronics Machine learning multi-sensor time series Multivariate analysis Predictive models Sensors Time dependence Time series Unsupervised anomaly detection
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
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