Work

A Fast Multiple Change-Point Detection Method via Generalized Nearly Isotonic Optimization

Year: 2026

Type: article

Abstract: In this paper, we study the generalized nearly isotonic optimization (GNIO) model and its dynamic programming solution (GNIO-DP). We introduce randomness into the GNIO-DP algorithm, enabling its first application to change-point detection and resulting in an O(n) complexity multiple change-point detection method. At the same time, we provide the theoretical properties of the change-point detection and prove the reliability and effectiveness of the GNIO-DP algorithm for this task. The simulation results show that our method has strong change-point detection ability. Compared with traditional methods, our method is faster in most scenarios.

Source: Entropy

DOI: 10.3390/e28090961

Authors: Luoxin Wang, Mengmeng Wang, Baisuo Jin, Yuehua Wu

Institutions: University of Science and Technology of China, Xinjiang University, Xinjiang Normal University, York University

Language: English

Topic: Advanced Statistical Process Monitoring

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funders: University of Science and Technology of China, Natural Sciences and Engineering Research Council of Canada