# Ensemble Binary Segmentation for irregularly spaced data with change-points

arXiv: https://arxiv.org/abs/2003.03649 · HTML: https://mlsanity.com/paper/2003.03649/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanation](https://mlsanity.com/paper/2601.02957/) (2026)

## Abstract (arXiv)

We propose a new technique for consistent estimation of the number and locations of the change-points in the structure of an irregularly spaced time series. The core of the segmentation procedure is the Ensemble Binary Segmentation method (EBS), a technique in which a large number of multiple change-point detection tasks using the Binary Segmentation (BS) method are applied on sub-samples of the data of differing lengths, and then the results are combined to create an overall answer. We do not restrict the total number of change-points a time series can have, therefore, our proposed method works well when the spacings between change-points are short. Our main change-point detection statistic is the time-varying Autoregressive Conditional Duration model on which we apply a transformation process in order to decorrelate it. To examine the performance of EBS we provide a simulation study for various types of scenarios. A proof of consistency is also provided. Our methodology is implemented in the R package eNchange, available to download from CRAN.