  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.
