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dc.contributor.authorLópez-Oriona, Ángel
dc.contributor.authorVilar, José
dc.date.accessioned2023-05-19T07:16:46Z
dc.date.available2023-05-19T07:16:46Z
dc.date.issued2023-06
dc.identifier.citationÁ. López-Oriona & J. A. Vilar, "Machine learning for multivariate time series with the R package mlmts", Neurocomputing, 537, pp. 210-235, 2023. doi:10.1016/j.neucom.2023.02.048es_ES
dc.identifier.urihttp://hdl.handle.net/2183/33095
dc.descriptionFinanciado para publicación en acceso aberto: Universidade da Coruña/CISUGes_ES
dc.description.abstract[Abstract]: Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals with univariate time series, multivariate time series have typically received much less attention. However, the development of machine learning algorithms for the latter objects has substantially increased in recent years. The R package mlmts attempts to provide a set of widespread data mining techniques for multivariate series. Several functions allowing the execution of clustering, classification, outlier detection and forecasting methods, among others, are included in the package. mlmts also incorporates a collection of multivariate time series datasets often used to test the performance of new classification algorithms. The main characteristics of the package are described and its use is illustrated through various examples. Practitioners from a wide variety of fields could benefit from the general framework provided by mlmts.es_ES
dc.description.sponsorshipThis research has been supported by the Ministerio de Economía y Competitividad (MINECO) grants MTM2017-82724-R and PID2020-113578RB-100, the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2020-14), and the Centro de Investigación del Sistema Universitario de Galicia, “CITIC” grant ED431G 2019/01; all of them through the European Regional Development Fund (ERDF). This work has received funding for open access charge by University of A Coruña/CISUG.es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2020-14es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherElsevier B.V.es_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSIONes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-100/ES/METODOS ESTADISTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORIA Y APLICACIONESes_ES
dc.relation.urihttps://doi.org/10.1016/j.neucom.2023.02.048es_ES
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)es_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectClassificationes_ES
dc.subjectClusteringes_ES
dc.subjectForecastinges_ES
dc.subjectmlmtses_ES
dc.subjectMultivariate time serieses_ES
dc.subjectOutlier detectiones_ES
dc.subjectR packagees_ES
dc.titleMachine learning for multivariate time series with the R package mlmtses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleNeurocomputinges_ES
UDC.volume537es_ES
UDC.startPage210es_ES
UDC.endPage235es_ES
dc.identifier.doi10.1016/j.neucom.2023.02.048


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