Zhu Yu, Xia Jie-lai, Wang Jing
Department of Epidemiology and Health Statistics, Anhui Medical University, Hefei 230032, China.
Zhonghua Liu Xing Bing Xue Za Zhi. 2009 Sep;30(9):964-8.
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.
“单自回归积分移动平均（ARIMA）模型”及“ARIMA-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。基于某城市2000年至2006年猩红热月发病率数据建立自回归积分移动平均模型。将ARIMA模型的拟合值作为GRNN的输入，实际值作为GRNN的输出。对GRNN进行训练后，比较单ARIMA模型与ARIMA-GRNN组合模型的效果。单ARIMA模型和ARIMA-GRNN组合模型的平均错误率（MER）分别为31.6%、28.7%，两个模型的决定系数（R²）分别为0.801、0.872。ARIMA-GRNN组合模型的拟合效果优于单ARIMA模型，在猩红热发病率等时间序列数据研究中具有实际价值。
