Bayesian Online Prediction of Change Points

Abstract

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 us to handle observation models which depend on the total segment duration, which is useful to model data sequences with temporal scaling. In addition, we extend the model by removing the i.i.d. assumption on the observation model parameters. The resulting inference algorithm for segment detection can be deployed in an online fashion, and we illustrate applications to synthetic and to two medical realworld data sets.

Change-Point Detection on Hierarchical Circadian Models

“…Additionally, unlike previous approaches, the non-exponential BOCPD model (Turner et al, 2013) explored applications to new families of distributions, where computing posterior probabilities is intractable and variational inference is therefore required. Another recent alternative is Agudelo-España et al (2019), which extends the BOCPD method to also predict the number of time steps until the next change point.…”