Gaussian Process Change Point Models

We combine Bayesian online change point detection with Gaussian processes to create a nonparametric time series model which can handle change points. The model can be used to locate change points in an online manner; and, unlike other Bayesian online change point detection algorithms, is applicable when temporal correlations in a regime are expected. We show three variations on how to apply Gaussian processes in the change point context, each with their own advantages. We present methods to reduce the computational burden of these models and demonstrate it on several real world data sets.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Yunus Saatci, Ryan Turner, Carl Edward Rasmussen
• Keywords: Bayesian Online Change, Change Point, Change Point Detection, ICML 2010, Machine Learning