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dc.contributor.authorBlázquez-García, A.
dc.contributor.authorConde, A.
dc.contributor.authorMori, U.
dc.contributor.authorLozano, J.A. 
dc.date.accessioned2021-07-07T13:58:35Z
dc.date.available2021-07-07T13:58:35Z
dc.date.issued2021
dc.identifier.issn0360-0300​
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1305
dc.description.abstractRecent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.en_US
dc.description.sponsorshipKK/2019-00095 IT1244-19 TIN2016-78365-R PID2019-104966GB-I00en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectOutlier detectionen_US
dc.subjectanomaly detectionen_US
dc.subjecttime seriesen_US
dc.subjectdata miningen_US
dc.subjecttaxonomyen_US
dc.subjectsoftwareen_US
dc.titleA Review on Outlier/Anomaly Detection in Time Series Dataen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleACM Computing Surveysen_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España