Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm

Abstract

We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series. It combines solution path generation based on the wild contrast maximisation principle, and an information criterion‐based model selection strategy termed gappy Schwarz algorithm. The former is well‐suited to separating shifts in the mean from fluctuations due to serial correlations, while the latter simultaneously estimates the dependence structure and the number of change points without performing the difficult task of estimating the level of the noise as quantified e.g. by the long‐run variance. We provide modular investigation into their theoretical properties and show that the combined methodology, named WCM.gSa, achieves consistency in estimating both the total number and the locations of the change points. The good performance of WCM.gSa is demonstrated via extensive simulation studies, and we further illustrate its usefulness by applying the methodology to London air quality data.

cpop: Detecting Changes in Piecewise-Linear Signals

“…We could continue as above, and choose between the segmentations by minimizing a penalized cost under an appropriate model for the noise; this type of approach is suggested for change in mean models by Cho and Fryzlewicz (2024). A simpler, albeit more qualitative approach, is to plot the residual sum of squares of the segmentation against the number of changepoints (Lebarbier 2005;Baudry, Maugis, and Michel 2012;Fearnhead and Rigaill 2020;Fryzlewicz 2020).…”