报告时间：2021年12月2日下午3：00

报告方式：腾讯会议 292101420

报告题目：A shape-based cutting and clustering algorithm for multiple change-point detection

报告摘要：The detection of multiple change-points without prior knowledge of the number and location of the change-points is investigated. 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.

报告人简介：庄丹，2019年博士毕业于西南财经大学。现任福建师范大学数学与统计学院讲师、硕导。主要从事变点分析、高维数据分析、统计方法应用研究等方向的研究。主持1项福建省教育厅项目，参与1项国家基金和1项国家电网福建电科院横向项目。发表10篇国内外期刊论文。