An adaptive cumulative sum method for monitoring integer-valued time-series data
An adaptive cumulative sum method for monitoring integer-valued time-series data
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DOI：
10.1080/08982112.2022.2044050
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发表时间：
2022-03
影响因子：
2
通讯作者：
O. A. Vanli;Rupert Giroux
中科院分区：
文献类型：
--
作者：
O. A. Vanli;Rupert Giroux
摘要本文提出了一种自适应的计数数据监测方法，用于检测计数数据时间序列中的阶跃和线性趋势变化。该数据表示使用季节性INGTON时间序列模型和指数平滑是用来估计水平或趋势变化的数据在累积和（AUCUM）检测器。在模拟研究中，所提出的方法相比，现有的CRACUUM方法，调整为一个特定的移位大小和检测阶跃移位和线性趋势的方法的能力进行了研究。以真实的传染病疫情数据为例，验证了该方法在公共卫生监测中的应用。
Abstract In this paper, we propose an adaptive CUSUM monitoring method for detecting step and linear trend changes in count-data time-series. The data is represented using a seasonal INGARCH time series model and an exponential smoother is used to estimate level or trend changes in the data in the cumulative-sum (CUSUM) detector. In a simulation study, the proposed approach is compared to existing CUSUM approaches that are tuned for a specific shift size and the ability of the methods to detect step shifts and linear trends is investigated. The application of the proposed method in public health surveillance is demonstrated using a real infectious disease count data set.