RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series

Robust anomaly detection is a requirement for monitoring complex modern systems with applications such as cyber-security, fraud prevention, and maintenance. These systems generate multiple correlated time series that are highly seasonal and noisy. This paper presents a novel unsupervised deep learni...

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Veröffentlicht in:	arXiv.org 2019-11
Hauptverfasser:	Khoshnevisan, Farzaneh, Fan, Zhewen
Format:	Artikel
Sprache:	eng
Schlagworte:	Anomalies Architecture Cybersecurity Fraud Machine learning Maintenance Multivariate analysis Time series
Online-Zugang:	Volltext
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