2024-03-29T13:55:49Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/574912022-11-28T20:00:43Zcom_10324_1151com_10324_931com_10324_894col_10324_1278
2022-11-26T23:05:36Z
urn:hdl:10324/57491
Robust fuzzy clustering of time series based on B-splines
D'Urso, Pierpaolo
García Escudero, Luis Ángel
De Giovanni, Livia
Vitale, Vincenzina
Mayo Iscar, Agustín
Producción Científica
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.
2022-11-26T23:05:36Z
2022-11-26T23:05:36Z
2021
info:eu-repo/semantics/article
International Journal of Approximate Reasoning, 136, 223-246.
https://uvadoc.uva.es/handle/10324/57491
https://doi.org/10.1016/j.ijar.2021.06.010
spa
info:eu-repo/semantics/openAccess
Elsevier