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dc.contributor.authorOregui, I.
dc.contributor.authorPérez, A. 
dc.contributor.authorDel Ser, J. 
dc.contributor.authorLozano, J.A. 
dc.date.accessioned2018-05-29T15:42:04Z
dc.date.available2018-05-29T15:42:04Z
dc.date.issued2017-09
dc.identifier.issn10.1007/978-3-319-71246-8_36
dc.identifier.urihttp://hdl.handle.net/20.500.11824/800
dc.description.abstractDynamic Time Warping is a well-known measure of dissimilarity between time series. Due to its flexibility to deal with non-linear distortions along the time axis, this measure has been widely utilized in machine learning models for this particular kind of data. Nowadays, the proliferation of streaming data sources has ignited the interest and attention of the scientific community around on-line learning models. In this work, we naturally adapt Dynamic Time Warping to the on-line learning setting. Specifically, we propose a novel on-line measure of dissimilarity for streaming time series which combines a warp constraint and a weighted memory mechanism to simplify the time series alignment and adapt to non-stationary data intervals along time. Computer simulations are analyzed and discussed so as to shed light on the performance and complexity of the proposed measure.en_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.subjectTime seriesen_US
dc.subjecton-line learningen_US
dc.subjectDynamic Time Warpingen_US
dc.titleOn-Line Dynamic Time Warping for Streaming Time Seriesen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.identifier.doi978-3-319-71246-8
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-319-71246-8_36en_US
dc.relation.projectIDES/1PE/SEV-2013-0323en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleMachine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. Lecture Notes in Computer Scienceen_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