# Multiple Change-point Detection: a Selective Overview

arXiv: https://arxiv.org/abs/1512.04093 · HTML: https://mlsanity.com/paper/1512.04093/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2015.

## Built on by (most cited first, 15 of 15)

- [Model Assessment and Selection under Temporal Distribution Shift](https://mlsanity.com/paper/2402.08672/) (2024)
- [Clustering Market Regimes using the Wasserstein Distance](https://mlsanity.com/paper/2110.11848/) (2021)
- [Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis](https://mlsanity.com/paper/2210.12235/) (2022)
- [Inference for Change Points in High Dimensional Mean Shift Models](https://mlsanity.com/paper/2107.09150/) (2021)
- [Population based change-point detection for the identification of homozygosity islands](https://mlsanity.com/paper/2111.10187/) (2021)
- [Efficient Estimation for Longitudinal Networks via Adaptive Merging](https://mlsanity.com/paper/2211.07866/) (2022)
- [Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models](https://mlsanity.com/paper/2305.18578/) (2023)
- [Change Point Detection with Copula Entropy based Two-Sample Test](https://mlsanity.com/paper/2403.07892/) (2024)
- [Online Planning of Power Flows for Power Systems Against Bushfires Using Spatial Context](https://mlsanity.com/paper/2404.13391/) (2024)
- [Continuous Optimization for Offline Change Point Detection and Estimation](https://mlsanity.com/paper/2407.03383/) (2024)
- [Perception Characteristics Distance: Measuring Stability and Robustness of Perception System in Dynamic Conditions under a Certain Decision Rule](https://mlsanity.com/paper/2506.09217/) (2025)
- [Vulnerability Disclosure through Adaptive Black-Box Adversarial Attacks on NIDS](https://mlsanity.com/paper/2506.20576/) (2025)
- [Accurate Evaluation of Quickest Changepoint Detectors via Non-parametric Survival Analysis](https://mlsanity.com/paper/2605.18798/) (2026)
- [Efficient Regression Models for Scan Statistics](https://mlsanity.com/paper/2608.22201/) (2026)
- [Distribution-free inference on the number of changepoints](https://mlsanity.com/paper/2609.08234/) (2026)

## Abstract (arXiv)

Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order to identify and understand shifts in trend, e.g., from a bull market to a bear market in finance or from a normal number of chromosome copies to an excessive number of chromosome copies in genetics. Thus, identifying multiple change points in a long, possibly very long, sequence is an important problem. In this article, we review both classical and new multiple change-point detection strategies. Considering the long history and the extensive literature on the change-point detection, we provide an in-depth discussion on a normal mean change-point model from aspects of regression analysis, hypothesis testing, consistency and inference. In particular, we present a strategy to gather and aggregate local information for change-point detection that has become the cornerstone of several emerging methods because of its attractiveness in both computational and theoretical properties.