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

Show full item record

Items in reposiTUm are protected by copyright, with all rights reserved, unless otherwise indicated.

Page view(s)

32
checked on May 1, 2023

Google ScholarTM

Check