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目的探讨异方差性时间序列模型在传染病疫情数据分析中的应用。方法分别采用ARIMA和AR-GARCH模型对某市淋病发病率月报数据进行建模和拟合。结果本资料构成的时间序列经检验具有明显异方差性,经模型比较和筛选,AR(1)-GARCH(0,1)模型能够较好的拟合本研究中传染病疫情时序数据。结论AR-GARCH模型适用于传染病疫情数据构成的异方差性时序数据分析。
Objective To explore the application of heteroscedasticity time series model in the data analysis of epidemic situation of infectious diseases. Methods ARIMA and AR-GARCH models were used to model and fit the monthly incidence of gonorrhea in a city. Results The time series of this data was significantly heteroscedastic. After comparison and screening of the models, the AR (1) -GARCH (0,1) model could well fit the epidemic timing data in this study. Conclusion The AR-GARCH model is suitable for analyzing heteroscedasticity data of epidemic situation data of infectious diseases.