Robust unsupervised anomaly detection via multi-time scale DCGANs with forgetting mechanism for industrial multivariate time series

Haoran Liang; Lei Song; Jianxing Wang; Lili Guo; Xuzhi Li; Ji Liang

doi:10.1016/j.neucom.2020.10.084

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Robust unsupervised anomaly detection via multi-time scale DCGANs with forgetting mechanism for industrial multivariate time series

Haoran Liang Lei Song Jianxing Wang Lili Guo Xuzhi Li Ji Liang

Year: 2020 Journal: Neurocomputing Vol: 423 Pages: 444-462 Publisher: Elsevier BV

DOI: 10.1016/j.neucom.2020.10.084

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Keywords:

Computer science Anomaly detection Artificial intelligence Multivariate statistics Robustness (evolution) Sliding window protocol Time series Pattern recognition (psychology) Data mining Forgetting Machine learning Series (stratigraphy) Deep learning Window (computing)

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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

Digital Media Forensic Detection

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Robust unsupervised anomaly detection via multi-time scale DCGANs with forgetting mechanism for industrial multivariate time series

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