A Contrastive Approach to Online Change Point Detection
Abstract
We suggest a novel procedure for online change point detection. Our approach expands an idea of maximizing a discrepancy measure between points from pre-change and post-change distributions. This leads to a flexible procedure suitable for both parametric and nonparametric scenarios. We prove non-asymptotic bounds on the average running length of the procedure and its expected detection delay. The efficiency of the algorithm is illustrated with numerical experiments on synthetic and real-world data sets.
Cite
Text
Puchkin and Shcherbakova. "A Contrastive Approach to Online Change Point Detection." Artificial Intelligence and Statistics, 2023.Markdown
[Puchkin and Shcherbakova. "A Contrastive Approach to Online Change Point Detection." Artificial Intelligence and Statistics, 2023.](https://mlanthology.org/aistats/2023/puchkin2023aistats-contrastive/)BibTeX
@inproceedings{puchkin2023aistats-contrastive,
title = {{A Contrastive Approach to Online Change Point Detection}},
author = {Puchkin, Nikita and Shcherbakova, Valeriia},
booktitle = {Artificial Intelligence and Statistics},
year = {2023},
pages = {5686-5713},
volume = {206},
url = {https://mlanthology.org/aistats/2023/puchkin2023aistats-contrastive/}
}