Comparative study of ARIMA model and GM (1,1) modelin predicting the incidence of diarrhea
SONG Yuan-yuan1,3, WANG Lei2, XIONG Tian1,3, HU Ying1,3
1. School of Health Science, Wuhan University, Wuhan, Hubei 430072, China; 2. Hubei Provincial Center for Disease Control and Prevention, Wuhan, Hubei 430072, China;
3. Center of Global Health, Wuhan University, Wuhan, Hubei 430072, China
Abstract：Objective The autoregressive integrated moving average model (ARIMA) and gray forecast model GM (1,1) were respectively applied to predicting the incidence of dysentery in Hubei Province. The prediction results of the two methods were compared to provide a basis for selecting a more appropriate method. Methods The ARIMA model and GM (1,1) model were established respectively based on the number of monthly cases and the number of annual cases from 2001 to 2015. The fitting effect was evaluated with the mean error rate (MER) and the coefficient of determination (R2), and the prediction effect was verified with the actual number of cases in 2016. The more accurate model was selected to predict the number of cases in 2017-2018. Results The established ARIMA model was SARIMA (1,0,0)(0,1,1)12, the established GM (1,1) model was(t+1)=-274 126.038e-0.067 467t+293 275.08, and the mean error rates (MER) of the two models were 3.55% and 14.78% respectively. The coefficients of determination (R2) were 0.993 and 0.960 respectively, and the residuals of the actual incidence in 2016 and the incidence predicted by the two models were 635 and 3,240 respectively. The relative errors were 16.54% and 84.38% respectively, and the incidence in 2017 and 2018 predicted by the ARIMA model was 4,286 and 4,011 respectively after all evaluation indexes having been taken into consideration.Conclusions According to the model fitness and prediction accuracy, the ARIMA model is superior to the GM (1,1) model. The ARIMA model has obvious advantages over the GM (1,1) model in predicting the number of dysentery cases in Hubei Province, and can process the data of time series more accurately. This prediction results are of practical value, and can provide a basis for health prevention and treatment.
宋媛媛, 王雷, 熊甜, 胡樱. ARIMA模型与GM(1,1)模型在痢疾发病数预测中的比较研究[J]. 实用预防医学, 2019, 26(7): 888-892.
SONG Yuan-yuan, WANG Lei, XIONG Tian, HU Ying. Comparative study of ARIMA model and GM (1,1) modelin predicting the incidence of diarrhea. , 2019, 26(7): 888-892.
2019, Vol. 26 
