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.
翻译:在时间序列数据分析中,实时(在线)探测变化点对金融、环境监测和医学等许多领域都非常感兴趣。实现这一点的一个有希望的手段是巴伊西亚在线变化点检测算法(BOCPD),在时间序列具有固定基线的特定情况下,该算法已经成功采用。然而,我们发现,当基线从初始状态不可逆转地从初始状态转移时,算法会挣扎。这是因为,与原始的BOCD算法相比,如果数据点在离原始基线较远的地方波动,可以检测变化点的敏感度就会降低。在本文中,我们不仅将原始的BOCD算法推广到基准不断转向未知值的时间序列中适用,而且将拟议扩展工作的原因直观化。为了证明拟议算法与原始状态相比的有效性,我们用两个真实世界数据集和六个合成数据集来研究这些算法。