VGG Based Unsupervised Anomaly Detection in Multivariate Time Series

Grzegorz Jabłoński

doi:10.1007/978-3-030-50936-1_107

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VGG Based Unsupervised Anomaly Detection in Multivariate Time Series

Grzegorz Jabłoński

Year: 2020 Advances in intelligent systems and computing Pages: 1287-1296 Publisher: Springer Nature

DOI: 10.1007/978-3-030-50936-1_107

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Keywords:

Multivariate statistics Anomaly detection Series (stratigraphy) Computer science Anomaly (physics) Artificial intelligence Pattern recognition (psychology) Machine learning Geology Physics

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Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

VGG Based Unsupervised Anomaly Detection in Multivariate Time Series

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