A shape-based multiple segmentation algorithm for change-point detection

Dan Zhuang, Qijing Yan, Shuangzhe Liu, Tiefeng Ma, Youbo Liu

Research output: Contribution to journalArticlepeer-review


We consider the detection and localization of change points for the off-line sequence of observations. Specifically, we propose a new multi-segmentation algorithm for detecting multiple change-points, named shape-based multiple segmentation algorithm, which is a generalization of binary segmentation. The proposed method is combined with deep mining on the shape information of the test statistics curve to overcome the Gaussian distribution hypothesis limitation and the limitation of traditional segmentation methods only being able to detect one change-point per stage. Combined with shape context, a robust testing statistic was developed via a shape-based descriptor statistic instead of the traditional CUSUM statistic. Then a data-driven threshold by the rightmost sudden-drop point is proposed, and the change points are further identified by single-peak identification. An efficient multiple segmentation based on a shape recognition procedure is implemented to locate change points. The effectiveness of the proposed procedure is illustrated using both synthetic data sets and real world data from electrical distribution networks.

Original languageEnglish
Article number108986
Pages (from-to)1-12
Number of pages12
JournalComputers and Industrial Engineering
Publication statusPublished - Feb 2023
Externally publishedYes


Dive into the research topics of 'A shape-based multiple segmentation algorithm for change-point detection'. Together they form a unique fingerprint.

Cite this