论文标题
贝叶斯在线变更点检测基线偏移
Bayesian Online Change Point Detection for Baseline Shifts
论文作者
论文摘要
在时间序列数据分析中，实时检测变化点（在线）在许多领域（例如金融，环境监测和医学）引起了极大的兴趣。实现这一目标的一种有希望的方法是贝叶斯在线变更点检测（BOCPD）算法，在特定情况下，该算法已成功采用，在特定情况下，时间序列的兴趣具有固定的基线。但是，我们发现，当基线不可逆地从其初始状态转移时，算法挣扎。这是因为使用原始的BOCPD算法，如果数据点在位置在距离原始基线相对较远的位置波动时，可以检测到变化点的灵敏度将被降低。在本文中，我们不仅将原始的BOCPD算法扩展到适用于时间序列的基线不断转移到未知值的时间序列上，而且还可以看到建议的扩展名为何可以。为了证明与原始算法相比，我们在两个现实世界数据集和六个合成数据集上检查了所提出的算法的功效。
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.
