Inductive Conformal Martingales for Change-Point Detection
This addresses the challenge of change-point detection without precise model knowledge, which is common in applied problems, but is incremental as it builds on existing conformal martingale frameworks.
The paper tackles the problem of quickest change-point detection in data streams by proposing a new method based on Inductive Conformal Martingales, which requires only independence and identical distribution of observations, and shows it is efficient under general conditions compared to standard methods and oracles.
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.