Abstract
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 language | English |
|---|---|
| Article number | 112623 |
| Pages (from-to) | 1-17 |
| Number of pages | 17 |
| Journal | Journal of Computational and Applied Mathematics |
| Volume | 369 |
| DOIs | |
| Publication status | Published - 1 May 2020 |
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A shape-based cutting and clustering algorithm for multiple change-point detection. / Zhuang, Dan; Liu, Youbo; Liu, Shuangzhe; Ma, Tiefeng; Ong, Seng huat.
In: Journal of Computational and Applied Mathematics, Vol. 369, 112623, 01.05.2020, p. 1-17.Research output: Contribution to journal › Article
TY - JOUR
T1 - A shape-based cutting and clustering algorithm for multiple change-point detection
AU - Zhuang, Dan
AU - Liu, Youbo
AU - Liu, Shuangzhe
AU - Ma, Tiefeng
AU - Ong, Seng huat
PY - 2020/5/1
Y1 - 2020/5/1
N2 - 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.
AB - 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.
KW - Local statistic
KW - Multiple change-points
KW - Scenarios reduction
KW - Shape recognition
UR - http://www.scopus.com/inward/record.url?scp=85075985871&partnerID=8YFLogxK
U2 - 10.1016/j.cam.2019.112623
DO - 10.1016/j.cam.2019.112623
M3 - Article
VL - 369
SP - 1
EP - 17
JO - Journal of Computational and Applied Mathematics
JF - Journal of Computational and Applied Mathematics
SN - 0377-0427
M1 - 112623
ER -