关键词: 肾综合征出血热, 广义回归神经网络, GM(1, 1)模型, 求和自回归滑动平均模型, 组合预测

Abstract: Objective To study the superiority and application of generalized regression neural network(GRNN) combination forecast model in the forecast of hemorrhagic fever with renal syndrome(HFRS) incidence. Methods Establish the GM(1,1) model and auto regressive integrated moving average(ARIMA) model based on the data of HFRS of Dandong, Shenyang and Chaoyang Liaoning province, from 1990 to 2001 respectively. The forecasting values of the two models were used as input of GRNN. Train the sample and forecast the value. Compare the forecasting effect of the three models. Results The mean error rate(MER) of GM(1,1) model, ARIMA model and GRNN combination model for Liaoning province were 13.5143%, 25.0814% and 5.5755% respectively. The R² of the three models were 0.8961, 0.6997 and 0.9837 respectively. The MER of GM(1,1) model, ARIMA model and GRNN combination model for Dandong were 19.7329%, 20.6275% and 14.0789% respectively. The R² values of three models were 0.8112, 0.7628 and 0.8750 respectively. The MER of GM(1,1) model, ARIMA model and GRNN combination model for Shenyang were 15.1421%, 18.0584% and 14.3592% respectively. The R² values of three models were 0.8757, 0.7889 and 0.8585 respectively. The MER of GM(1,1) model, ARIMA model and GRNN combination model for Chaoyang were 51.5090%, 28.6593% and 28.5927% respectively. The R²values of three models were 0.7863, 0.8291 and 0.7753 respectively. The forecasting efficacy of combination model for Liaoning province was better than other two single models. For the forecasting efficacy of Shenyang, the GRNN combination model and the GM(1,1) model were similar, and the ARIMA model was the worst. The incidence of HFRS for Chaoyang is not fit for the establishment of the models we mentioned above. Conclusion GRNN combination model had more advantage in the forecast of small sample and the forecasting efficacy was better than GM(1,1) model and ARIMA model, which had practical value in the treatment of time series data such as the incidence of HFRS.