Multivariate Change-Point Detection Using Feature-Based Inductive Conformal Martingales

Charalambos Eliades, Harris Papadopoulos
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:633-651, 2026.

Abstract

Change-point detection in sequential data can be formulated as the problem of identifying violations of exchangeability as observations arrive over time. In this work, we study feature-based change-point detection within the framework of Inductive Conformal Martingales, which are well known for providing theoretical validity guarantees on the probability of false alarms. We construct nonconformity measures that quantify how unusual the features of a test instance are. In particular, our proposed nonconformity measure is based on an ellipsoidal k-nearest neighbour density estimator combined with Mahalanobis distance. We evaluate the proposed method on four real-world datasets in which an artificial change-point is inserted, as well as on an air-quality dataset where the goal is to estimate possible sensor recalibration points. We also compare our approach with two competing nonparametric multivariate change-detection methods with false-alarm control mechanisms. The results illustrate the potential of feature-based conformal martingales for practical sequential change-point detection in multivariate data.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-eliades26a, title = {Multivariate Change-Point Detection Using Feature-Based Inductive Conformal Martingales}, author = {Eliades, Charalambos and Papadopoulos, Harris}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {633--651}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/eliades26a/eliades26a.pdf}, url = {https://proceedings.mlr.press/v329/eliades26a.html}, abstract = {Change-point detection in sequential data can be formulated as the problem of identifying violations of exchangeability as observations arrive over time. In this work, we study feature-based change-point detection within the framework of Inductive Conformal Martingales, which are well known for providing theoretical validity guarantees on the probability of false alarms. We construct nonconformity measures that quantify how unusual the features of a test instance are. In particular, our proposed nonconformity measure is based on an ellipsoidal k-nearest neighbour density estimator combined with Mahalanobis distance. We evaluate the proposed method on four real-world datasets in which an artificial change-point is inserted, as well as on an air-quality dataset where the goal is to estimate possible sensor recalibration points. We also compare our approach with two competing nonparametric multivariate change-detection methods with false-alarm control mechanisms. The results illustrate the potential of feature-based conformal martingales for practical sequential change-point detection in multivariate data.} }
Endnote
%0 Conference Paper %T Multivariate Change-Point Detection Using Feature-Based Inductive Conformal Martingales %A Charalambos Eliades %A Harris Papadopoulos %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-eliades26a %I PMLR %P 633--651 %U https://proceedings.mlr.press/v329/eliades26a.html %V 329 %X Change-point detection in sequential data can be formulated as the problem of identifying violations of exchangeability as observations arrive over time. In this work, we study feature-based change-point detection within the framework of Inductive Conformal Martingales, which are well known for providing theoretical validity guarantees on the probability of false alarms. We construct nonconformity measures that quantify how unusual the features of a test instance are. In particular, our proposed nonconformity measure is based on an ellipsoidal k-nearest neighbour density estimator combined with Mahalanobis distance. We evaluate the proposed method on four real-world datasets in which an artificial change-point is inserted, as well as on an air-quality dataset where the goal is to estimate possible sensor recalibration points. We also compare our approach with two competing nonparametric multivariate change-detection methods with false-alarm control mechanisms. The results illustrate the potential of feature-based conformal martingales for practical sequential change-point detection in multivariate data.
APA
Eliades, C. & Papadopoulos, H.. (2026). Multivariate Change-Point Detection Using Feature-Based Inductive Conformal Martingales. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:633-651 Available from https://proceedings.mlr.press/v329/eliades26a.html.

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