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Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention

AuthID
P-00Q-9AG

Author(s)

Pereira, J

Silveira, M

Editor(s)

Wani, MA; Kantardzic, M; Sayedmouchaweh, M; Gama, J; Lughofer, E

Document Type

Proceedings Paper

Year published

2018

Published

in 2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA)

Pages: 1275-1282 (8)

Conference

17Th Ieee International Conference on Machine Learning and Applications (Ieee Icmla), Date: DEC 17-20, 2018, Location: Orlando, FL, Sponsors: IEEE, Assoc Machine Learning & Applicat

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Publication Identifiers

DOI: 10.1109/icmla.2018.00207

Scopus: 2-s2.0-85062225633

Wos: WOS:000463034400199

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Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention

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