Details

Autor(en) / Beteiligte

• Parpoula, Christina
• Karagrigoriou, Alex

Titel

On optimal segmentation and parameter tuning for multiple change-point detection and inference

Ist Teil von

• Journal of statistical computation and simulation, 2022-12, Vol.92 (18), p.3789-3816

Ort / Verlag

Abingdon: Taylor & Francis

Erscheinungsjahr

2022

Link zum Volltext

Quelle

Taylor & Francis

Beschreibungen/Notizen

• 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.