论文标题
在存在滋扰变化的情况下，流行病变化点检测
Epidemic changepoint detection in the presence of nuisance changes
论文作者
论文摘要
许多时间序列问题具有流行性变化 - 参数偏离背景基线的段。可以通过现有检测方法以原则性的方式估算此类更改的数量和位置，从而提供背景级别稳定且已知。但是，实际数据通常包含背景级别的滋扰变化，这会干扰标准估计技术。此外，这种变化通常与目标段仅在持续时间内有所不同，并且在检测结果中显示为错误警报。为了解决这些问题，我们提出了一个两级检测器，该检测器模拟并分离了滋扰和信号变化。作为该方法的一部分，我们开发了一种新的，有效的方法来同时估计未知但固定的背景水平并检测流行病的变化。建立了所提出方法的分析和计算特性，包括一致性和收敛性。我们通过模拟证明，我们的两级检测器在滋扰过程中提供了对变更点的准确估计，而其他最先进的检测器则失败。使用现实世界中的基因组和人口数据集，我们证明我们的方法可以识别和定位目标事件，同时分离季节性变化和实验人工制品。
Many time series problems feature epidemic changes - segments where a parameter deviates from a background baseline. The number and location of such changes can be estimated in a principled way by existing detection methods, providing that the background level is stable and known. However, practical data often contains nuisance changes in background level, which interfere with standard estimation techniques. Furthermore, such changes often differ from the target segments only in duration, and appear as false alarms in the detection results. To solve these issues, we propose a two-level detector that models and separates nuisance and signal changes. As part of this method, we developed a new, efficient approach to simultaneously estimate unknown, but fixed, background level and detect epidemic changes. The analytic and computational properties of the proposed methods are established, including consistency and convergence. We demonstrate via simulations that our two-level detector provides accurate estimation of changepoints under a nuisance process, while other state-of-the-art detectors fail. Using real-world genomic and demographic datasets, we demonstrate that our method can identify and localise target events while separating out seasonal variations and experimental artefacts.
