  In time series data analysis, detecting change points on a real-time basis
(online) is of great interest in many areas, such as finance, environmental
monitoring, and medicine. One promising means to achieve this is the Bayesian
online change point detection (BOCPD) algorithm, which has been successfully
adopted in particular cases in which the time series of interest has a fixed
baseline. However, we have found that the algorithm struggles when the baseline
irreversibly shifts from its initial state. This is because with the original
BOCPD algorithm, the sensitivity with which a change point can be detected is
degraded if the data points are fluctuating at locations relatively far from
the original baseline. In this paper, we not only extend the original BOCPD
algorithm to be applicable to a time series whose baseline is constantly
shifting toward unknown values but also visualize why the proposed extension
works. To demonstrate the efficacy of the proposed algorithm compared to the
original one, we examine these algorithms on two real-world data sets and six
synthetic data sets.
