dc.contributor.author | D'Urso, Pierpaolo | |
dc.contributor.author | García Escudero, Luis Ángel | |
dc.contributor.author | De Giovanni, Livia | |
dc.contributor.author | Vitale, Vincenzina | |
dc.contributor.author | Mayo Iscar, Agustín | |
dc.date.accessioned | 2022-11-26T23:05:36Z | |
dc.date.available | 2022-11-26T23:05:36Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | International Journal of Approximate Reasoning, 136, 223-246. | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/57491 | |
dc.description | Producción Científica | es |
dc.description.abstract | Four different approaches to robust fuzzy clustering of time series are presented and compared with respect to other existent approaches. These approaches are useful to cluster time series when outlying values are found in these time series, which is often the rule in most real data applications. Arepresentation of the time series by using B-splines is considered and, later, robust fuzzy clustering
methods are applied on the B-splines fitted coefficients. Feasible algorithms for implementing these methodologies are presented. Asimulation study shows how these methods are useful to deal with contaminating time series and also switching time series due to fuzziness. A real data analysis example on financial data is also presented. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | spa | es |
dc.publisher | Elsevier | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.title | Robust fuzzy clustering of time series based on B-splines | es |
dc.type | info:eu-repo/semantics/article | es |
dc.identifier.doi | 10.1016/j.ijar.2021.06.010 | es |
dc.peerreviewed | SI | es |
dc.description.project | Spanish Ministerio de Economía y Competitividad, grant MTM2017-86061-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León and FEDER, grant VA005P17 and VA002G18. | es |
dc.type.hasVersion | info:eu-repo/semantics/draft | es |