Monitoring of count data time series: Cumulative sum change detection in Poisson integer valued GARCH models
Monitoring of count data time series: Cumulative sum change detection in Poisson integer valued GARCH models
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DOI：
10.1080/08982112.2018.1508696
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发表时间：
2018-11
影响因子：
2
通讯作者：
O. A. Vanli;Rupert Giroux;Eren Erman Ozguven;J. Pignatiello
中科院分区：
文献类型：
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作者：
O. A. Vanli;Rupert Giroux;Eren Erman Ozguven;J. Pignatiello
摘要提出了一种计数数据时间序列的累积和(CUSUM)监测方法。利用具有Poisson偏差的季节性整值广义自回归条件异方差(INGARCH(1，1))时间序列模型，建立了考虑时间相关性和季节性的似然比检验公式。仿真研究表明，在序列相关性或季节性普遍存在的应用中，所提出的累积和监控方法可以显著提高性能。以实际交通事故计数为例，说明了该方法在道路安全改进中的应用。
Abstract This article presents a cumulative sum (CUSUM) monitoring approach for count-data time series. A seasonal integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH(1,1)) time series model with Poisson deviates is used to develop a likelihood ratio test formulation to detect changes in the process accounting for temporal correlations and seasonality. Simulation studies show that the proposed CUSUM monitoring approach can provide significantly improved performance in applications where serial correlation or seasonality is prevalent. A case study with real traffic crash counts is presented to illustrate the application of the proposed methodology for roadway safety improvement.