Sangam: A Confluence of Knowledge Streams

MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series

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dc.creator Liu, Dongyu
dc.creator Alnegheimish, Sarah
dc.creator Zytek, Alexandra
dc.creator Veeramachaneni, Kalyan
dc.date 2022-11-04T18:35:03Z
dc.date 2022-11-04T18:35:03Z
dc.date 2022-04-07
dc.date 2022-11-03T00:21:39Z
dc.date.accessioned 2023-02-17T19:56:10Z
dc.date.available 2023-02-17T19:56:10Z
dc.identifier 2573-0142
dc.identifier https://hdl.handle.net/1721.1/146166
dc.identifier Liu, Dongyu, Alnegheimish, Sarah, Zytek, Alexandra and Veeramachaneni, Kalyan. 2022. "MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series." PACM on Human-Computer Interaction.
dc.identifier PUBLISHER_CC
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/242052
dc.format application/pdf
dc.language en
dc.publisher ACM
dc.relation https://doi.org/10.1145/3512950
dc.relation PACM on Human-Computer Interaction
dc.rights Creative Commons Attribution 4.0 International license
dc.rights https://creativecommons.org/licenses/by/4.0/
dc.rights The author(s)
dc.source ACM
dc.title MTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Series
dc.type Article
dc.type http://purl.org/eprint/type/ConferencePaper


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