Record link: 
http://hdl.handle.net/20.500.12708/139958
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Title: 
Improving deep learning based anomaly detection onmultivariate time series through separated anomalyscoring
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Citation: 
Lundström, A., O’Nils, M., Qureshi, F., & Jantsch, A. (2022). Improving deep learning based anomaly detection onmultivariate time series through separated anomalyscoring. IEEE Access, 10, 108194–108204. https://doi.org/10.1109/ACCESS.2022.3213038
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Publisher DOI: 
10.1109/ACCESS.2022.3213038
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Publication Type: 
Article - Original Research Article
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Language: 
English
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Authors: 
Lundström, Adam 
O'Nils, Mattias 
Qureshi, Faisal 
Jantsch, Axel 
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Organisational Unit: 
E384-02 - Forschungsbereich Systems on Chip 
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Journal: 
IEEE Access 
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ISSN: 
2169-3536
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Date (published): 
2022
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Number of Pages: 
11
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Publisher: 
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Peer reviewed: 
Yes
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Keywords: 
Anomaly detection; anomaly scoring; Deep learning (DL); multivariate time series (MVTS)
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Research Areas: 
Computer Engineering and Software-Intensive Systems: 100%
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Science Branch: 
2020 - Elektrotechnik, Elektronik, Informationstechnik: 100%
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