On optimal segmentation and parameter tuning for multiple change-point detection and inference. Issue 18 (12th December 2022)

• Record Type: Journal Article
• Authors: Parpoula, Christina; Karagrigoriou, Alex
• Is Part Of: Journal of statistical computation and simulation. Volume 92:Issue 18(2022)
• Page Start: 3789
• Page End: 3816
• Publication Date: 2022-12-12
• Subjects: Change-point analysis -- dynamic programming -- recursive optimization -- segmentation space -- tuning parameters; 62-07 -- 62-09 -- 62M10 -- 62P10; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2022.2083127
• Languages: English
• ISSNs: 0094-9655

Abstract

Abstract : Change-point analysis is the task of finding abrupt (and significant) changes in the underlying model of a signal or time series. Change-point detection methods typically involve specifying the maximum number of segments to search for and the minimum segment length. However, there is no objective way to pre-specify these two parameters, and it mostly depends upon the particular application. Within this framework, a recursive optimization algorithm is developed that is capable of exploring and fine tuning these two input parameters, and optimally segmenting a time series. This multiple change-point detection technique therefore addresses a wide class of real-life contexts and problems where the identification of optimal level shifts in a time series is the main goal. Extensive simulation results are presented and a real-life example is given to illustrate the implementation of the developed scheme in practice and to unfold its capabilities. Concluding remarks and suggestions for future research are also provided.