Instituto de Investigación
en Matemáticas


Robust Clustering for Time Series using Spectral Densities and Functional Data Analysis

Joaquín Ortega (CIMAT - Guanajuato (México))

Fecha: 05/07/2017 09:00
Lugar: Seminario del Departamento de Estadística, Facultad de Ciencias
Grupo: G.I.R. Probabilidad y Estadística Matemática

In this talk a robust clustering algorithm for stationary time series is presented. 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