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Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods

Cabrieto, Jedelyn and Tuerlinckx, Francis and Kuppens, Peter and Grassmann, Mariel and Ceulemans, Eva (2016) Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods. Behavior Research Methods. Springer. DOI: 10.3758/s13428-016-0754-9 ISSN 1554-351X

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Abstract

Abstract Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.

Item URL in elib:https://elib.dlr.de/108401/
Document Type:Article
Title:Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Cabrieto, JedelynUNSPECIFIEDUNSPECIFIED
Tuerlinckx, FrancisUNSPECIFIEDUNSPECIFIED
Kuppens, PeterUNSPECIFIEDUNSPECIFIED
Grassmann, Marielmariel.grassmann (at) dlr.deUNSPECIFIED
Ceulemans, EvaUNSPECIFIEDUNSPECIFIED
Date:2016
Journal or Publication Title:Behavior Research Methods
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.3758/s13428-016-0754-9
Publisher:Springer
ISSN:1554-351X
Status:Published
Keywords:Change point detection . Correlation changes . Multivariate time series . DeCon . ROBPCA
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:air traffic management and operations
DLR - Research area:Aeronautics
DLR - Program:L AO - Air Traffic Management and Operation
DLR - Research theme (Project):L - Human factors and safety in Aeronautics
Location: Hamburg
Institutes and Institutions:Institute of Aerospace Medicine > Aviation and Space Psychology
Deposited By: Witt, Andrea
Deposited On:07 Dec 2016 15:13
Last Modified:08 Mar 2018 18:39

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