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dc.contributor.authorLiu, Dongyu
dc.contributor.authorAlnegheimish, Sarah
dc.contributor.authorZytek, Alexandra
dc.contributor.authorVeeramachaneni, Kalyan
dc.date.accessioned2022-11-04T18:35:03Z
dc.date.available2022-11-04T18:35:03Z
dc.date.issued2022-04-07
dc.identifier.issn2573-0142
dc.identifier.urihttps://hdl.handle.net/1721.1/146166
dc.publisherACMen_US
dc.relation.isversionofhttps://doi.org/10.1145/3512950en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceACMen_US
dc.titleMTV: Visual Analytics for Detecting, Investigating, and Annotating Anomalies in Multivariate Time Seriesen_US
dc.typeArticleen_US
dc.identifier.citationLiu, 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.contributor.departmentMIT Schwarzmann College of Computing
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systems
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Data, Systems, and Society
dc.relation.journalPACM on Human-Computer Interactionen_US
dc.identifier.mitlicensePUBLISHER_CC
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2022-11-03T00:21:39Z
dc.language.rfc3066en
dc.rights.holderThe author(s)
dspace.date.submission2022-11-03T00:21:40Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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