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Quantile Cross-Spectral Density: A Novel and Effective Tool for Clustering Multivariate Time Series
dc.contributor.author | López-Oriona, Ángel | |
dc.contributor.author | Vilar, José | |
dc.date.accessioned | 2021-11-09T19:58:17Z | |
dc.date.available | 2021-11-09T19:58:17Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | LÓPEZ-ORIONA, Ángel; VILAR, José A. Quantile cross-spectral density: A novel and effective tool for clustering multivariate time series. Expert Systems with Applications, 2021, vol. 185, p. 115677. https://doi.org/10.1016/j.eswa.2021.115677 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/28826 | |
dc.description | Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG | es_ES |
dc.description.abstract | [Abstract] Clustering of multivariate time series is a central problem in data mining with applications in many fields. Frequently, the clustering target is to identify groups of series generated by the same multivariate stochastic process. Most of the approaches to address this problem include a prior step of dimensionality reduction which may result in a loss of information or consider dissimilarity measures based on correlations and cross-correlations but ignoring the serial dependence structure. We propose a novel approach to measure dissimilarity between multivariate time series aimed at jointly capturing both cross dependence and serial dependence. Specifically, each series is characterized by a set of matrices of estimated quantile cross-spectral densities, where each matrix corresponds to a pair of quantile levels. Then the dissimilarity between every couple of series is evaluated by comparing their estimated quantile cross-spectral densities, and the pairwise dissimilarity matrix is taken as starting point to develop a partitioning around medoids algorithm. Since the quantile-based cross-spectra capture dependence in quantiles of the joint distribution, the proposed metric has a high capability to discriminate between high-level dependence structures. An extensive simulation study shows that our clustering procedure outperforms a wide range of alternative methods and exhibits robustness to noise distribution besides being computationally efficient. A real data application involving bivariate financial time series illustrates the usefulness of the proposed approach. The procedure is also applied to cluster nonstationary series from the UEA multivariate time series classification archive. | es_ES |
dc.description.sponsorship | This 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 Universidade da Coruña/CISUG | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C-2020-14 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G 2019/01 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/MTM2017-82724-R/ES/INFERENCIA ESTADISTICA FLEXIBLE PARA DATOS COMPLEJOS DE GRAN VOLUMEN Y DE ALTA DIMENSION | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/METODOS ESTADISTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORIA Y APLICACIONES | |
dc.relation.uri | https://doi.org/10.1016/j.eswa.2021.115677 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Multivariate time series | es_ES |
dc.subject | Clustering | es_ES |
dc.subject | Dissimilarity measure | es_ES |
dc.subject | Quantile cross-spectral density | es_ES |
dc.subject | S&P 500 | es_ES |
dc.subject | UEA archive | es_ES |
dc.title | Quantile Cross-Spectral Density: A Novel and Effective Tool for Clustering Multivariate Time Series | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Expert Systems with Applications | es_ES |
UDC.volume | 185 | es_ES |
UDC.startPage | 115677 | es_ES |
dc.identifier.doi | 10.1016/j.eswa.2021.115677 |
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