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

Abstract

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.