Testing for the Presence of Correlation Changes in a Multivariate Time Series: A Permutation Based Approach

Abstract

Detecting abrupt correlation changes in multivariate time series is crucial in many application fields such as signal processing, functional neuroimaging, climate studies, and financial analysis. To detect such changes, several promising correlation change tests exist, but they may suffer from severe loss of power when there is actually more than one change point underlying the data. To deal with this drawback, we propose a permutation based significance test for Kernel Change Point (KCP) detection on the running correlations. Given a requested number of change points K, KCP divides the time series into K + 1 phases by minimizing the within-phase variance. The new permutation test looks at how the average within-phase variance decreases when K increases and compares this to the results for permuted data. The results of an extensive simulation study and applications to several real data sets show that, depending on the setting, the new test performs either at par or better than the state-of-the art significance tests for detecting the presence of correlation changes, implying that its use can be generally recommended.

Detecting long-lived autodependency changes in a multivariate system via change point detection and regime switching models

“…Going back to the differences in the obtained results, these naturally follow from the differences between the methods. As holds for all change point detection methods, KCP-AR can only locate longer-lasting change points that demarcate events occurring for a certain period 36 . The regime switching AR(1) method, on the other hand, can unravel both longer-lasting switches bounded by change points and short-lived switches.…”

An Objective, Comprehensive and Flexible Statistical Framework for Detecting Early Warning Signs of Mental Health Problems

“…The method is statistically sound, well suited for explorative usage and has been shown to identify valid change points in a large variety of applications [6-8]. Here, we were able to reveal change points in a number of running statistics of affective symptoms, detecting EWS before relapse into depression.…”

“…In this paper, we propose a statistical framework, kernel change point detection (KCP [6-8]), that detects EWS by testing whether summary statistics of repeatedly measured symptoms (e.g., means, variances, correlations, autocorrelations) change across time. The method, a detailed description of which can be found in the online supplementary material (for all online suppl.…”

Change-Point Methods on a Sequence of Graphs

“…The type of change is abrupt when P commutes from Q 0 to Q 1 in a single time-step. As finite sequence g(1, T ) is given, to address the detection of a single change in stationarity, we propose to adopt a Change Point Method (CPM) [8], [9], [10], [11], which relies on a series of two-sample statistical tests [s, p val ] = Test(g(1, t − 1), g(t, T )) applied to the T − 1 pairs of subsets g(1, t − 1) = {g 1 , . .…”

“…Accordingly, detecting a change is more difficult in the latter case. In this paper, we consider classes 0, 6,8,10,12,14,16,18, and 20 as reported in Table I.…”