# Random Forests for Change Point Detection

arXiv: https://arxiv.org/abs/2205.04997 · HTML: https://mlsanity.com/paper/2205.04997/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 66 references that are in the map.
- First posted on arXiv 10 May 2022.

## Builds on (most cited first, 16 of 16)

- [Revisiting Classifier Two-Sample Tests](https://mlsanity.com/paper/1610.06545/) (2016)
- [Kernel Change-point Detection with Auxiliary Deep Generative Models](https://mlsanity.com/paper/1901.06077/) (2019)
- [A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1306.4933/) (2013)
- [High-dimensional changepoint estimation via sparse projection](https://mlsanity.com/paper/1606.06246/) (2016)
- [Multiscale Change-Point Inference](https://mlsanity.com/paper/1301.7212/) (2013)
- [ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data](https://mlsanity.com/paper/1309.3295/) (2013)
- [On the Use of Random Forest for Two-Sample Testing](https://mlsanity.com/paper/1903.06287/) (2019)
- [Optimal nonparametric multivariate change point detection and localization](https://mlsanity.com/paper/1910.13289/) (2019)
- [Seeded Binary Segmentation: A general methodology for fast and optimal change point detection](https://mlsanity.com/paper/2002.06633/) (2020)
- [Change-Point Estimation in High-Dimensional Markov Random Field Models](https://mlsanity.com/paper/1405.6176/) (2014)
- [Heterogeneous Change Point Inference](https://mlsanity.com/paper/1505.04898/) (2015)
- [Change point detection for graphical models in the presence of missing values](https://mlsanity.com/paper/1907.05409/) (2019)
- [Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries](https://mlsanity.com/paper/2010.10194/) (2020)
- [Graph-based multiple change-point detection](https://mlsanity.com/paper/2110.01170/) (2021)
- [Segmentation of the Poisson and negative binomial rate models: a penalized estimator](https://mlsanity.com/paper/1301.2534/) (2013)
- [Seeded intervals and noise level estimation in change point detection: A discussion of Fryzlewicz (2020)](https://mlsanity.com/paper/2006.12806/) (2020)

## Built on by (most cited first, 14 of 14)

- [Automatic Change-Point Detection in Time Series via Deep Learning](https://mlsanity.com/paper/2211.03860/) (2022)
- [The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology](https://mlsanity.com/paper/2404.11341/) (2024)
- [CP-PINNs: Data-Driven Changepoints Detection in PDEs Using Online Optimized Physics-Informed Neural Networks](https://mlsanity.com/paper/2208.08626/) (2022)
- [Latent Neural Stochastic Differential Equations for Change Point Detection](https://mlsanity.com/paper/2208.10317/) (2022)
- [Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures](https://mlsanity.com/paper/2306.15835/) (2023)
- [Causal Discovery-Driven Change Point Detection in Time Series](https://mlsanity.com/paper/2407.07290/) (2024)
- [Penalty Learning for Optimal Partitioning using Multilayer Perceptron](https://mlsanity.com/paper/2408.00856/) (2024)
- [Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failures](https://mlsanity.com/paper/2410.22594/) (2024)
- [Solve Smart, Not Often: Policy Learning for Costly MILP Re-solving](https://mlsanity.com/paper/2509.23470/) (2025)
- [Noise-contrastive Online Change Point Detection](https://mlsanity.com/paper/2206.10143/) (2022)
- [Inferring Change Points in High-Dimensional Regression via Approximate Message Passing](https://mlsanity.com/paper/2404.07864/) (2024)
- [Score-based change point detection via tracking the best of infinitely many experts](https://mlsanity.com/paper/2408.14073/) (2024)
- [Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data](https://mlsanity.com/paper/2503.09541/) (2025)
- [Distribution-free inference on the number of changepoints](https://mlsanity.com/paper/2609.08234/) (2026)

## Abstract (arXiv)

We propose a novel multivariate nonparametric multiple change point detection method using classifiers. We construct a classifier log-likelihood ratio that uses class probability predictions to compare different change point configurations. We propose a computationally feasible search method that is particularly well suited for random forests, denoted by changeforest. However, the method can be paired with any classifier that yields class probability predictions, which we illustrate by also using a k-nearest neighbor classifier. We prove that it consistently locates change points in single change point settings when paired with a consistent classifier. Our proposed method changeforest achieves improved empirical performance in an extensive simulation study compared to existing multivariate nonparametric change point detection methods. An efficient implementation of our method is made available for R, Python, and Rust users in the changeforest software package.