A shape-based cutting and clustering algorithm for multiple change-point detection

Dan Zhuang, Youbo Liu, Shuangzhe Liu, Tiefeng Ma, Seng huat Ong

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


The detection of multiple change-points without prior knowledge of the number and location of the change-points is investigated in this paper. By making full use of the geometric information of local statistics, a new change-point detection algorithm called the shape-based cutting and clustering (SCC) algorithm is established. There are three key techniques in the proposed SCC procedure: data-driven threshold, adaptive bandwidth and single peak recognition. Our simulation results show that the proposed method is highly competitive in terms of computational speed and effectiveness. In order to validate the feasibility of the proposed algorithm, we apply the methodology to an operational problem in renewable integrated electrical distribution networks. The results of the real data analysis illustrate the effectiveness of the proposed algorithm.

Original languageEnglish
Article number112623
Pages (from-to)1-17
Number of pages17
JournalJournal of Computational and Applied Mathematics
Publication statusPublished - 1 May 2020


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