# Multiple-change-point detection for high dimensional time series via sparsified binary segmentation

arXiv: https://arxiv.org/abs/1611.08639 · HTML: https://mlsanity.com/paper/1611.08639/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2016.

## Built on by (most cited first, 24 of 24)

- [Testing Stationarity and Change Point Detection in Reinforcement Learning](https://mlsanity.com/paper/2203.01707/) (2022)
- [Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference](https://mlsanity.com/paper/2110.08989/) (2021)
- [Segmentation of high dimensional means over multi-dimensional change points and connections to regression trees](https://mlsanity.com/paper/2105.10017/) (2021)
- [Latent Neural Stochastic Differential Equations for Change Point Detection](https://mlsanity.com/paper/2208.10317/) (2022)
- [A Constant-per-Iteration Likelihood Ratio Test for Online Changepoint Detection for Exponential Family Models](https://mlsanity.com/paper/2302.04743/) (2023)
- [Causal Discovery-Driven Change Point Detection in Time Series](https://mlsanity.com/paper/2407.07290/) (2024)
- [Subspace Change-Point Detection via Low-Rank Matrix Factorisation](https://mlsanity.com/paper/2110.04044/) (2021)
- [Graph Convolution Neural Network For Weakly Supervised Abnormality Localization In Long Capsule Endoscopy Videos](https://mlsanity.com/paper/2110.09110/) (2021)
- [Detection and Estimation of Structural Breaks in High-Dimensional Functional Time Series](https://mlsanity.com/paper/2304.07003/) (2023)
- [An Automated Machine Learning Approach for Detecting Anomalous Peak Patterns in Time Series Data from a Research Watershed in the Northeastern United States Critical Zone](https://mlsanity.com/paper/2309.07992/) (2023)
- [Bayesian Autoregressive Online Change-Point Detection with Time-Varying Parameters](https://mlsanity.com/paper/2407.16376/) (2024)
- [Unsupervised Text Segmentation via Kernel Change-Point Detection on Sentence Embeddings](https://mlsanity.com/paper/2601.18788/) (2026)
- [Identification of brain states, transitions, and communities using functional MRI](https://mlsanity.com/paper/2101.10617/) (2021)
- [Unsupervised Shot Boundary Detection for Temporal Segmentation of Long Capsule Endoscopy Videos](https://mlsanity.com/paper/2110.09067/) (2021)
- [Distribution estimation and change-point estimation for time series via DNN-based GANs](https://mlsanity.com/paper/2211.14577/) (2022)
- [Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data](https://mlsanity.com/paper/2302.00781/) (2023)
- [Change Point Detection with Conceptors](https://mlsanity.com/paper/2308.06213/) (2023)
- [The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning](https://mlsanity.com/paper/2308.13035/) (2023)
- [Inferring Change Points in High-Dimensional Regression via Approximate Message Passing](https://mlsanity.com/paper/2404.07864/) (2024)
- [Neural Network-Based Change Point Detection for Large-Scale Time-Evolving Data](https://mlsanity.com/paper/2503.09541/) (2025)
- [Active Learning for Multiple Change Point Detection in Non-stationary Time Series with Deep Gaussian Processes](https://mlsanity.com/paper/2505.20452/) (2025)
- [Consistent Kernel Change-Point Detection under m-Dependence for Text Segmentation](https://mlsanity.com/paper/2510.03437/) (2025)
- [TimePred: efficient and interpretable offline change point detection for high volume data -- with application to industrial process monitoring](https://mlsanity.com/paper/2512.01562/) (2025)
- [High-dimensional nonparametric changepoint detection via low-rank degree-two density projection](https://mlsanity.com/paper/2608.13922/) (2026)

## Abstract (arXiv)

Time series segmentation, a.k.a. multiple change-point detection, is a well-established problem. However, few solutions are designed specifically for high-dimensional situations. In this paper, our interest is in segmenting the second-order structure of a high-dimensional time series. In a generic step of a binary segmentation algorithm for multivariate time series, one natural solution is to combine CUSUM statistics obtained from local periodograms and cross-periodograms of the components of the input time series. However, the standard "maximum" and "average" methods for doing so often fail in high dimensions when, for example, the change-points are sparse across the panel or the CUSUM statistics are spuriously large. In this paper, we propose the Sparsified Binary Segmentation (SBS) algorithm which aggregates the CUSUM statistics by adding only those that pass a certain threshold. This "sparsifying" step reduces the impact of irrelevant, noisy contributions, which is particularly beneficial in high dimensions. In order to show the consistency of SBS, we introduce the multivariate Locally Stationary Wavelet model for time series, which is a separate contribution of this work.