RT info:eu-repo/semantics/article
T1 Robust fuzzy clustering of time series based on B-splines
A1 D'Urso, Pierpaolo
A1 García Escudero, Luis Ángel
A1 De Giovanni, Livia
A1 Vitale, Vincenzina
A1 Mayo Iscar, Agustín
AB 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.
PB Elsevier
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/57491
UL https://uvadoc.uva.es/handle/10324/57491
LA spa
NO International Journal of Approximate Reasoning, 136, 223-246.
NO Producción Científica
DS UVaDOC
RD 29-abr-2024