摘要
【导读】探讨单纯求和自回归滑动平均（ARIMA）模型和求和自回归滑动平均模型与广义回归神经网络（GRNN）组合模型在猩红热发病率研究中的应用。该研究对某市2000--2006年猩红热月发病率资料建立ARIMA模型，然后将其拟合值作为GRNN的输入，实际值作为网络的输出训练网络，然后比较两个模型的效果。结果表明，单纯ARIMA模型和组合模型的平均误差率（MER）分别为31．6％、28．7％；决定系数（R^2）分别为0．801、0．872。组合模型的效果要优于单纯ARIMA模型，可以用于发病率的拟合与预测。
Application of the ＇single auto regressive integrated moving average （ARIMA） model＇ and the ＇ARIMA-generalized regression neural network （GRNN） combination model＇ in the research of the incidence of scarlet fever. Establish the auto regressive integrated moving average model based on the data of the monthly incidence on scarlet fever of one city, from 2000 to 2006. The fitting values of the ARIMA model was used as input of the GRNN, and the actual values were used as output of the GRNN. After training the GRNN, the effect of the single ARIMA model and the ARIMA-GRNN combination model was then compared. The mean error rate （MER） of the single ARIMA model and the ARIMA-GRNN combination model were 31.6%, 28.7% respectively and the determination coefficient （R^2） of the two models were 0.801,0.872 respectively. The fitting efficacy of the ARIMA-GRNN combination model was better than the single ARIMA, which had practical value in the research on time series data such as the incidence of scarlet fever.
出处
《中华流行病学杂志》
CAS
CSCD
北大核心
2009年第9期964-968,共5页
Chinese Journal of Epidemiology
基金
基金项目：安徽省教育厅人文社科重点项目（2009sk192zd）
安徽医科大学学术技术带头人科研资助
关键词
猩红热
自回归滑动平均模型
广义回归神经网络
Scarlet fever
Auto regressive integrated moving average model
Generalized regression neural network
