# Sequential change-point detection based on nearest neighbors

arXiv: https://arxiv.org/abs/1604.03611 · HTML: https://mlsanity.com/paper/1604.03611/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2016.

## Built on by (most cited first, 11 of 11)

- [Online Kernel CUSUM for Change-Point Detection](https://mlsanity.com/paper/2211.15070/) (2022)
- [A Log-Linear Non-Parametric Online Changepoint Detection Algorithm based on Functional Pruning](https://mlsanity.com/paper/2302.02718/) (2023)
- [Online Change Point Detection for Weighted and Directed Random Dot Product Graphs](https://mlsanity.com/paper/2201.11222/) (2022)
- [Optimal network online change point localisation](https://mlsanity.com/paper/2101.05477/) (2021)
- [Sequential Multivariate Change Detection with Calibrated and Memoryless False Detection Rates](https://mlsanity.com/paper/2108.00883/) (2021)
- [Sequential change detection via backward confidence sequences](https://mlsanity.com/paper/2302.02544/) (2023)
- [Gradient-Based Spectral Embeddings of Random Dot Product Graphs](https://mlsanity.com/paper/2307.13818/) (2023)
- [Monitoring Machine Learning Models: Online Detection of Relevant Deviations](https://mlsanity.com/paper/2309.15187/) (2023)
- [Optimal Online Change Detection via Random Fourier Features](https://mlsanity.com/paper/2505.17789/) (2025)
- [On the Detection of Non-Cooperative RISs: Scan B-Testing via Deep Support Vector Data Description](https://mlsanity.com/paper/2411.03237/) (2024)
- [An Efficient Likelihood Ratio Test for Online Changepoint Detection in the Presence of Autocorrelation](https://mlsanity.com/paper/2607.16106/) (2026)

## Abstract (arXiv)

We propose a new framework for the detection of change-points in online, sequential data analysis. The approach utilizes nearest neighbor information and can be applied to sequences of multivariate observations or non-Euclidean data objects, such as network data. Different stopping rules are explored, and one specific rule is recommended due to its desirable properties. An accurate analytic approximation of the average run length is derived for the recommended rule, making it an easy off-the-shelf approach for real multivariate/object sequential data monitoring applications. Simulations reveal that the new approach has better performance than likelihood-based approaches for high dimensional data. The new approach is illustrated through a real dataset in detecting global structural changes in social networks.