Clustering time series by linear dependency

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dc.contributor.author Alonso, Andres M.
dc.contributor.author Peña, Daniel
dc.date.accessioned 2021-06-28T08:15:45Z
dc.date.available 2021-06-28T08:15:45Z
dc.date.issued 2019-07-15
dc.identifier.bibliographicCitation Alonso, A. M. & Peña, D. (2019). Clustering time series by linear dependency. Statistics and Computing, 29(4), pp. 655–676.
dc.identifier.issn 0960-3174
dc.identifier.uri http://hdl.handle.net/10016/32941
dc.description.abstract We present a new way to find clusters in large vectors of time series by using a measure of similarity between two time series, the generalized cross correlation. This measure compares the determinant of the correlation matrix until some lag k of the bivariate vector with those of the two univariate time series. A matrix of similarities among the series based on this measure is used as input of a clustering algorithm. The procedure is automatic, can be applied to large data sets and it is useful to find groups in dynamic factor models. The cluster method is illustrated with some Monte Carlo experiments and a real data example.
dc.format.extent 22
dc.language.iso eng
dc.publisher Springer
dc.rights © Springer Science+Business Media, LLC, part of Springer Nature 2018
dc.subject.other Correlation coefficient
dc.subject.other Correlation matrix
dc.subject.other Dynamic factor models
dc.subject.other Unsupervised learning
dc.title Clustering time series by linear dependency
dc.type article
dc.subject.eciencia Estadística
dc.identifier.doi https://doi.org/10.1007/s11222-018-9830-6
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ECO2015-66593-P
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 655
dc.identifier.publicationissue 4
dc.identifier.publicationlastpage 676
dc.identifier.publicationtitle Statistics and Computing
dc.identifier.publicationvolume 29
dc.identifier.uxxi AR/0000024995
dc.contributor.funder Ministerio de Economía y Competitividad (España)
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