Unsupervised anomaly detection with LSTM autoencoders using statistical data-filtering

To address one of the most challenging industry problems, we develop an enhanced training algorithm for anomaly detection in unlabelled sequential data such as time-series. We propose the outputs of a well-designed system are drawn from an unknown probability distribution, U, in normal conditions. W...

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Veröffentlicht in:	Applied soft computing Jg. 108; S. 107443
Hauptverfasser:	Maleki, Sepehr, Maleki, Sasan, Jennings, Nicholas R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2021
Schlagworte:	Anomaly detection Autoencoders Changepoint detection IoT LSTM networks Changepoint detection Autoencoders LSTM networks Anomaly detection IoT
ISSN:	1568-4946, 1872-9681
Online-Zugang:	Volltext
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