title: Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods.
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.
authors.affiliation: Quantitative Psychology and Individual Differences Research Group, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, B-3000, Belgium. Jed.Cabrieto@ppw.kuleuven.be.
authors.forename: Jedelyn
authors.initials: J
authors.lastname: Cabrieto
authors.affiliation: Quantitative Psychology and Individual Differences Research Group, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, B-3000, Belgium.
authors.forename: Francis
authors.initials: F
authors.lastname: Tuerlinckx
authors.affiliation: Quantitative Psychology and Individual Differences Research Group, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, B-3000, Belgium.
authors.forename: Peter
authors.initials: P
authors.lastname: Kuppens
authors.affiliation: Health Psychology Research Group, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, B-3000, Belgium.|Department of Aviation and Space Psychology, German Aerospace Center (DLR), Sportallee 54a, Hamburg, 22335, Germany.
authors.forename: Mariel
authors.initials: M
authors.lastname: Grassmann
authors.affiliation: Quantitative Psychology and Individual Differences Research Group, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, B-3000, Belgium.
authors.forename: Eva
authors.initials: E
authors.lastname: Ceulemans
mesh_terms: D000465:Algorithms; D006801:Humans; D065186:Interrupted Time Series Analysis; D013223:Statistics as Topic; D018709:Statistics, Nonparametric
keywords: Change point detection; Correlation changes; DeCon; Multivariate time series; ROBPCA