RT info:eu-repo/semantics/article
T1 Time Series, Spectral Densities and Robust Functional Clustering
A1 Rivera-García, Diego
A1 García Escudero, Luis Ángel
A1 Mayo Iscar, Agustín
A1 Ortega, Joaquín
AB In this work, a robust clustering algorithm for stationary time series is proposed.The algorithmis based on the use of estimated spectral densities, which are considered as functional data, asthe basic characteristic of stationary time series for clustering purposes. A robust algorithmfor functional data is then applied to the set of spectral densities. Trimming techniques andrestrictions on the scatter within groups reduce the effect of noise in the data and help toprevent the identification of spurious clusters. The procedure is tested in a simulation studyand is also applied to a real data set.
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/69766
UL https://uvadoc.uva.es/handle/10324/69766
LA spa
NO Neural Processing Letters, 52(1), 135-152.
NO Producción Científica
DS UVaDOC
RD 24-dic-2024