Application of ARIMA and hybrid ARIMA-GRNN models in forecasting AIDS incidence in China
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摘要: 目的 探究适用于预测我国艾滋病月发病人数的模型，为艾滋病的预防提供参考。方法 收集2011年1月至2017年12月我国艾滋病月发病报告人数资料，建立自回归移动平均（autoregressive integrated moving average，ARIMA）模型及广义回归神经网络（generalized regression neural network，GRNN）模型与ARIMA模型的联合。用2018年1月至5月艾滋病月发病数评估该模型预测效果。结果 艾滋病月发病数呈明显季节性，拟建立ARIMA（1，1，1）（0，1，1）12模型对我国艾滋病月发病报告数进行预测。建立ARIMA-GRNN模型的光滑因子为0.021。ARIMA-GRNN模型拟合及预测误差均低于ARIMA模型。结论 ARIMA（1，1，1）（0，1，1）12和ARIMA-GRNN模型均能较好地拟合并预测我国艾滋病月发病人数，但联合模型的效果更优。
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关键词:
- 艾滋病 /
- 自回归求和移动平均乘积季节模型 /
- 广义回归神经网络 /
- 预测
Abstract: Objective To explore a suitable model in forecasting monthly incidence of AIDS in China and offer some references for AIDS prevention. Methods The data was collected from January 2011 to December 2017 to build the autoregressive integrated moving average (ARIMA) model and hybrid ARIMA-generalized regression neural network (GRNN) model. The data from January to May 2018 was used to evaluate two models' forecasting performance. Result A strong seasonal variation can be seen and ARIMA(1,1,1) (0,1,1)12 was selected as the most suitable model to forecast the AIDS incidence in China. The smooth factor of hybrid model was 0.021. The hybrid ARIMA-GRNN model had better fitting and forecasting performances than single ARIMA model. Conclusion Both of the two models were suitable in forecasting monthly incidence of AIDS and the hybrid model was better than single ARIMA model. -
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