Bayesian Online Prediction of Change Points
This work addresses the need for predicting future change points in real-time data analysis, particularly in medical domains, but is incremental as it builds upon an existing algorithm.
The authors tackled the problem of online detection of change points by extending the Bayesian Online Change Point Detection algorithm to predict future change points, specifically inferring the residual time until the next change, and demonstrated its application on synthetic and medical datasets.
Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences. We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next change point (i.e., the residual time). This enables to handle observation models which depend on the total segment duration, which is useful to model data sequences with temporal scaling. The resulting inference algorithm for segment detection can be deployed in an online fashion, and we illustrate applications to synthetic and to two medical real-world data sets.