  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.
