Inductive Conformal Martingales for Change-Point Detection

TL;DR

This paper introduces a new change-point detection method using Inductive Conformal Martingales that works under general conditions without requiring precise change-point models, outperforming traditional methods in practical scenarios.

Contribution

The paper proposes a novel change-point detection approach based on Inductive Conformal Martingales that only requires data independence and identical distribution, broadening applicability.

Findings

Effective detection under general conditions

Outperforms classical methods in practical scenarios

Works with imprecise pre- and post-change models

Abstract

We consider the problem of quickest change-point detection in data streams. Classical change-point detection procedures, such as CUSUM, Shiryaev-Roberts and Posterior Probability statistics, are optimal only if the change-point model is known, which is an unrealistic assumption in typical applied problems. Instead we propose a new method for change-point detection based on Inductive Conformal Martingales, which requires only the independence and identical distribution of observations. We compare the proposed approach to standard methods, as well as to change-point detection oracles, which model a typical practical situation when we have only imprecise (albeit parametric) information about pre- and post-change data distributions. Results of comparison provide evidence that change-point detection based on Inductive Conformal Martingales is an efficient tool, capable to work under quite general conditions unlike traditional approaches.