A Contrastive Approach to Online Change Point Detection

Nikita Puchkin, Valeriia Shcherbakova
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:5686-5713, 2023.

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 this Paper

BibTeX
@InProceedings{pmlr-v206-puchkin23a, title = {A Contrastive Approach to Online Change Point Detection}, author = {Puchkin, Nikita and Shcherbakova, Valeriia}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {5686--5713}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/puchkin23a/puchkin23a.pdf}, url = {https://proceedings.mlr.press/v206/puchkin23a.html}, 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.} }
Endnote
%0 Conference Paper %T A Contrastive Approach to Online Change Point Detection %A Nikita Puchkin %A Valeriia Shcherbakova %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-puchkin23a %I PMLR %P 5686--5713 %U https://proceedings.mlr.press/v206/puchkin23a.html %V 206 %X 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.
APA
Puchkin, N. & Shcherbakova, V.. (2023). A Contrastive Approach to Online Change Point Detection. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:5686-5713 Available from https://proceedings.mlr.press/v206/puchkin23a.html.

Related Material