Show simple item record

dc.contributor.authorGil Lertxundi, Amaia
dc.contributor.authorQuartulli, Marco Francesco
dc.contributor.authorGarcía Olaizola, Igor
dc.contributor.authorSierra Araujo, Basilio
dc.date.accessioned2021-05-04T08:15:04Z
dc.date.available2021-05-04T08:15:04Z
dc.date.issued2021
dc.identifier.citationIEEE Access 9 : 44390-44401 (2021)es_ES
dc.identifier.issn2169-3536
dc.identifier.urihttp://hdl.handle.net/10810/51297
dc.description.abstractTransmitting and storing large volumes of dynamic / time series data collected by modern sensors can represent a significant technological challenge. A possibility to mitigate this challenge is to effectively select a subset of significant data points in order to reduce data volumes without sacrificing the quality of the results of the subsequent analysis. This paper proposes a method for adaptively identifying optimal data point selection algorithms for sensor time series on a window-by-window basis. Thus, this contribution focuses on quantifying the effect of the application of data selection algorithms to time series windows. The proposed approach is first used on multiple synthetically generated time series obtained by concatenating multiple sources one after the other, and then validated in the entire UCR time series public data archivees_ES
dc.description.sponsorshipThis work was supported in part by the 3KIA Project through ELKARTEK, Basque Governmentes_ES
dc.language.isoenges_ES
dc.publisherIEEE-Institute of Electrical and Electronics Engineerses_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectdata selectiones_ES
dc.subjectmachine learninges_ES
dc.subjectoptimizationes_ES
dc.subjecttime serieses_ES
dc.titleTowards Smart Data Selection from Tithe Series Using Statistical Methodses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.holderThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)es_ES
dc.rights.holderAtribución 3.0 España*
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/9380329es_ES
dc.identifier.doi10.1109/ACCESS.2021.3066686
dc.departamentoesCiencia de la computación e inteligencia artificiales_ES
dc.departamentoeuKonputazio zientziak eta adimen artifizialaes_ES


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)
Except where otherwise noted, this item's license is described as This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)