# An Efficient Likelihood Ratio Test for Online Changepoint Detection in the Presence of Autocorrelation

arXiv: https://arxiv.org/abs/2607.16106 · HTML: https://mlsanity.com/paper/2607.16106/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 17 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [High-dimensional, multiscale online changepoint detection](https://mlsanity.com/paper/2003.03668/) (2020)
- [Sequential change-point detection based on nearest neighbors](https://mlsanity.com/paper/1604.03611/) (2016)
- [Fast Online Changepoint Detection via Functional Pruning CUSUM statistics](https://mlsanity.com/paper/2110.08205/) (2021)
- [A Log-Linear Non-Parametric Online Changepoint Detection Algorithm based on Functional Pruning](https://mlsanity.com/paper/2302.02718/) (2023)
- [Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation](https://mlsanity.com/paper/2506.17442/) (2025)
- [Detecting Abrupt Changes in the Presence of Local Fluctuations and Autocorrelated Noise](https://mlsanity.com/paper/2005.01379/) (2020)
- [A Constant-per-Iteration Likelihood Ratio Test for Online Changepoint Detection for Exponential Family Models](https://mlsanity.com/paper/2302.04743/) (2023)
- [A communication-efficient, online changepoint detection method for monitoring distributed sensor networks](https://mlsanity.com/paper/2403.18549/) (2024)
- [Searching for long faint astronomical high energy transients: a data driven approach](https://mlsanity.com/paper/2303.15936/) (2023)

## Abstract (arXiv)

Changepoint detection methods have seen considerable development in recent years, with online algorithms capable of identifying structural changes in streaming data in near real time. However, the majority of existing methods are designed under the assumption of IID observations, rendering them susceptible to either more false positives or longer detection delays when applied to data exhibiting temporal dependence, a common feature of many real-world data streams. In this article, we extend the generalised likelihood-ratio (GLR) statistic to autoregressive processes of order p, and adapt the focus algorithm to develop a computationally efficient online change detector. The resulting AR(p)-focus algorithm achieves an average computational cost of O(log n) per iteration, making it suitable for high-frequency data streams. Through simulation studies, the proposed approach is seen achieving greater detection power than IID-based tests when the underlying data exhibit temporal correlation. We further illustrate the practical utility of AR(p)-focus through an application to a real-world telecommunications dataset.