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dc.contributor.author | Rivera-García, Diego | |
dc.contributor.author | García Escudero, Luis Ángel | |
dc.contributor.author | Mayo Iscar, Agustín | |
dc.contributor.author | Ortega, Joaquín | |
dc.date.accessioned | 2024-09-15T21:43:06Z | |
dc.date.available | 2024-09-15T21:43:06Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Neural Processing Letters, 52(1), 135-152. | es |
dc.identifier.uri | https://uvadoc.uva.es/handle/10324/69766 | |
dc.description | Producción Científica | es |
dc.description.abstract | In this work, a robust clustering algorithm for stationary time series is proposed.The algorithm is based on the use of estimated spectral densities, which are considered as functional data, as the basic characteristic of stationary time series for clustering purposes. A robust algorithm for functional data is then applied to the set of spectral densities. Trimming techniques and restrictions on the scatter within groups reduce the effect of noise in the data and help to prevent the identification of spurious clusters. The procedure is tested in a simulation study and is also applied to a real data set. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | spa | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.title | Time Series, Spectral Densities and Robust Functional Clustering | es |
dc.type | info:eu-repo/semantics/article | es |
dc.identifier.doi | https://doi.org/10.1007/s11063-018-9926-1 | es |
dc.identifier.publicationfirstpage | 135 | es |
dc.identifier.publicationissue | 52 | es |
dc.identifier.publicationlastpage | 152 | es |
dc.identifier.publicationvolume | 1 | 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, Grants VA005P17 and VA002G18. | es |
dc.type.hasVersion | info:eu-repo/semantics/draft | es |