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Robust Methods for Soft Clustering of Multidimensional Time Series
dc.contributor.author | López-Oriona, Ángel | |
dc.contributor.author | D'Urso, Pierpaolo | |
dc.contributor.author | Vilar, José | |
dc.contributor.author | Lafuente Rego, Borja Raúl | |
dc.date.accessioned | 2022-01-11T18:24:08Z | |
dc.date.available | 2022-01-11T18:24:08Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | López-Oriona, Á.; D’Urso, P.; Vilar, J.A.; Lafuente-Rego, B. Robust Methods for Soft Clustering of Multidimensional Time Series. Eng. Proc. 2021, 7, 60. https://doi.org/10.3390/engproc2021007060 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/29353 | |
dc.description | Presented at the 4th XoveTIC Conference, A Coruña, Spain, 7–8 October 2021. | es_ES |
dc.description.abstract | [Abstract] Three robust algorithms for clustering multidimensional time series from the perspective of underlying processes are proposed. The methods are robust extensions of a fuzzy C-means model based on estimates of the quantile cross-spectral density. Robustness to the presence of anomalous elements is achieved by using the so-called metric, noise and trimmed approaches. Analyses from a wide simulation study indicate that the algorithms are substantially effective in coping with the presence of outlying series, clearly outperforming alternative procedures. The usefulness of the suggested methods is also highlighted by means of a specific application. | es_ES |
dc.description.sponsorship | This research has been supported by MINECO (MTM2017-82724-R and PID2020-113578RB-100), the Xunta de Galicia (ED431C-2020-14), and “CITIC” (ED431G 2019/01). | 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 | MDPI | 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/10.3390/engproc2021007060 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Multidimensional time series | es_ES |
dc.subject | Fuzzy C-means | es_ES |
dc.subject | Unsupervised learning | es_ES |
dc.title | Robust Methods for Soft Clustering of Multidimensional Time Series | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Engineering Proceedings | es_ES |
UDC.volume | 7 | es_ES |
UDC.issue | 1 | es_ES |
UDC.startPage | 60 | es_ES |
dc.identifier.doi | 10.3390/engproc2021007060 |