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Applications of Box-Jenkins (Seasonal ARIMA) and GARCH models to dengue incidence in Thailand

机译:Box-Jenkins(季节性ARIMA)和GARCH模型在泰国登革热发病率中的应用

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In this paper, we focus on monthly number of dengue cases in Thailand using the univariate Box-Jenkins (seasonal ARIMA) and GARCH models. There are 3 types of dengue i.e. dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome (DSS). These series are fitted with adjustment by population size and seasonal index. For each type, the best model is choosen by Akaike’s Information Criteria (AIC) and Schwartz’s Bayesian Criteria (SBC). A comparison of the fitted Box-Jenkins and GARCH models are presented using root mean square error (RMSE) and mean absolute percentage error (MAPE). The results showed that the best fitted for the univariate Box-Jenkins models of DF, DHF and DSS cases are seasonal ARIMA(0, 1, 1) × (0, 1, 1)12, ARIMA(0, 1, 1) × (0, 1, 1)12 and ARIMA(0, 1, 3) × (0, 1, 1)12, respectively, while the best fitted GARCH models of DF, DHF and DSS cases are AR(1)-GARCH(1, 1) adjusted seasonal components, AR(8)-ARCH(1) removed seasonal components and AR(1)-ARCH(1) adjusted seasonal components, consequently. Further, from a comparison the GARCH model outperforms than the univariate Box-Jenkins (seasonal ARIMA) model. Removing seasonal components technique increased efficiency of fitting model in GARCH method while adjustment by population size did not give a significantly difference result to both methods.
机译:在本文中,我们使用单变量Box-Jenkins(季节性ARIMA)和GARCH模型关注泰国的登革热每月病例数。登革热有3种类型,即登革热(DF),登革出血热(DHF)和登革热休克综合征(DSS)。这些系列根据人口规模和季节指数进行了调整。对于每种类型,最佳模型均由Akaike的Information Criteria(AIC)和Schwartz的Bayesian Criteria(SBC)选择。使用均方根误差(RMSE)和平均绝对百分比误差(MAPE)对拟合的Box-Jenkins模型和GARCH模型进行了比较。结果表明,DF,DHF和DSS案例的单变量Box-Jenkins模型的最佳拟合是季节性ARIMA(0,1,1)×(0,1,1)12,ARIMA(0,1,1)× (0,1,1)12和ARIMA(0,1,3)×(0,1,1)12,而最适合DF,DHF和DSS情况的GARCH模型是AR(1)-GARCH(因此,1,1)调整了季节性成分,AR(8)-ARCH(1)删除了季节性成分,而AR(1)-ARCH(1)调整了季节性成分。此外,通过比较,GARCH模型的表现优于单变量Box-Jenkins(季节性ARIMA)模型。去除季节性成分技术可以提高GARCH方法拟合模型的效率,而按人口规模进行调整并不能使两种方法的结果产生显着差异。

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