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Neural Processing Letters
Published in: Neural Processing Letters 1/2020

01-10-2018

Time Series, Spectral Densities and Robust Functional Clustering

Authors: D. Rivera-García, L. A. García-Escudero, A. Mayo-Iscar, J. Ortega

Published in: Neural Processing Letters | Issue 1/2020

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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.
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Metadata
Title
Time Series, Spectral Densities and Robust Functional Clustering
Authors
D. Rivera-García
L. A. García-Escudero
A. Mayo-Iscar
J. Ortega
Publication date
01-10-2018
Publisher
Springer US
Published in
Neural Processing Letters / Issue 1/2020
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9926-1

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