Online Neural Networks for Change-Point Detection
Abstract
Moments when a time series changes its behavior are called change points. Occurrence of change point implies that the state of the system is altered and its timely detection might help to prevent unwanted consequences. In this paper, we present two change-point detection approaches based on neural networks and online learning. These algorithms demonstrate linear computational complexity and are suitable for change-point detection in large time series. We compare them with the best known algorithms on various synthetic and real world data sets. Experiments show that the proposed methods outperform known approaches. We also prove the convergence of the algorithms to the optimal solutions and describe conditions rendering current approach more powerful than offline one.
Cite
@article{arxiv.2010.01388,
title = {Online Neural Networks for Change-Point Detection},
author = {Mikhail Hushchyn and Kenenbek Arzymatov and Denis Derkach},
journal= {arXiv preprint arXiv:2010.01388},
year = {2026}
}
Comments
This version of the article has been submitted to the journal but is not the Version of Record and does not reflect peer-review improvements, post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10994-026-07000-6