[Comparison of predictive effect between the single auto regressive integrated moving average (ARIMA) model and the ARIMA-generalized regression neural network (GRNN) combination model on the incidence of scarlet fever].
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.
Zhu
Yu
Y
Department of Epidemiology and Health Statistics, Anhui Medical University, Hefei 230032, China.
Xia
Jie-lai
JL
Wang
Jing
J
D002681:China / Q000453:epidemiology; D004812:Epidemiologic Methods; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012541:Scarlet Fever / Q000453:epidemiology*