<div class="csl-bib-body">
<div class="csl-entry">Lundström, A., O’Nils, M., Qureshi, F., & Jantsch, A. (2022). Improving deep learning based anomaly detection onmultivariate time series through separated anomalyscoring. <i>IEEE Access</i>, <i>10</i>, 108194–108204. https://doi.org/10.1109/ACCESS.2022.3213038</div>
</div>
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dc.identifier.issn
2169-3536
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/139958
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dc.language.iso
en
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dc.publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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dc.relation.ispartof
IEEE Access
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dc.subject
Anomaly detection
en
dc.subject
anomaly scoring
en
dc.subject
Deep learning (DL)
en
dc.subject
multivariate time series (MVTS)
en
dc.title
Improving deep learning based anomaly detection onmultivariate time series through separated anomalyscoring
en
dc.type
Article
en
dc.type
Artikel
de
dc.contributor.affiliation
Mid Sweden University, Sweden
-
dc.contributor.affiliation
Mid Sweden University, Sweden
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dc.description.startpage
108194
-
dc.description.endpage
108204
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dc.type.category
Original Research Article
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tuw.container.volume
10
-
tuw.journal.peerreviewed
true
-
tuw.peerreviewed
true
-
wb.publication.intCoWork
International Co-publication
-
tuw.researchTopic.id
I2
-
tuw.researchTopic.name
Computer Engineering and Software-Intensive Systems