Temporal convolutional autoencoder for unsupervised anomaly detection in time series

Learning temporal patterns in time series remains a challenging task up until today. Particularly for anomaly detection in time series, it is essential to learn the underlying structure of a system’s normal behavior. Periodic or quasiperiodic signals with complex temporal patterns make the problem e...

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Bibliographic Details

Published in:	Applied soft computing Vol. 112; p. 107751
Main Authors:	Thill, Markus, Konen, Wolfgang, Wang, Hao, Bäck, Thomas
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.11.2021
Subjects:	Anomaly detection Autoencoder Deep learning Mahalanobis distance TCN Deep learning Autoencoder Anomaly detection TCN Mahalanobis distance
ISSN:	1568-4946, 1872-9681
Online Access:	Get full text
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