Robust Clustering for Time Series Using Spectral Densities and Functional Data Analysis
Machine Learning
2017-02-09 v1
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.
Cite
@article{arxiv.1702.02165,
title = {Robust Clustering for Time Series Using Spectral Densities and Functional Data Analysis},
author = {Diego Rivera-García and Luis Angel García-Escudero and Agustín Mayo-Iscar and Joaquín Ortega},
journal= {arXiv preprint arXiv:1702.02165},
year = {2017}
}