Sequential change-point detection for generalized Ornstein-Uhlenbeck processes

TL;DR

This paper develops and compares sequential change-point detection methods for generalized Ornstein-Uhlenbeck processes with periodic drift, providing insights into their effectiveness through numerical experiments.

Contribution

It introduces two new detection methods tailored for these processes and evaluates their performance with various parameter settings.

Findings

Both methods effectively detect change-points in simulated data.

Performance varies depending on process parameters and detection thresholds.

Numerical experiments demonstrate the practical applicability of the proposed methods.

Abstract

In this article, we study sequential change-point methods for discretely observed generalized Ornstein-Uhlenbeck processes with periodic drift. Two detection methods are proposed, and their respective performance is studied through numerical experiments for several choices of parameters.