BPNN神经网络模型和SARIMA模型在荆州市乙类传染病发病数中的预测效果比较

目的 评价BPNN神经网络模型和季节性差分自回归滑动平均模型(seasonal autoregressive integrated moving average,SARIMA)在乙类传染病发病数中的预测效果.方法 利用荆州市2005年1月—2017年12月的乙类传染病逐月发病数作为拟合数据,建立BPNN神经网络模型和S...

A Comparison of the Prediction Effects of Back Propagation Neural Network Model and Seasonal Autoregressive Integrated Moving Average Model in the Caseload of Class B Notifiable Diseases in Jingzhou City

Objective Evaluating the effect of the Back Propagation Neural Network(BPNN)model and Seasonal Autoregressive Integrated Moving Average(SARIMA)model in the prediction of class B notifiable diseases in Jingzhou City.Methods Data of class B notifiable diseases monthly caseload from January 2005 to December 2017 in Jingzhou City were used to establish BPNN model and SARIMA model,and predicted the monthly cases between January and April 2018 and compared with the actual value.Mean absolute percentage error(MAPE),R²,root mean square error(RMSE)and mean absolute error(MAE)were used to evaluate the fit and prediction effects.Results The ARIMA[0,1,(12)](1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence.In the fitting phase,the MAPE,R2,RMSE and MAE fitted by BPNN model and SARIMA model were 3.92%,0.92,82.29,61.93 and 7.16%,0.49,149.93,118.10.The MAPE,R2,RMSE and MAE predicted by BPNN model and SARIMA model were 11.84%,0.23,180.33,94.76 and 21.96%,-0.91,633.94,251.19.Conclusion The BPNN model showed better class B notifiable diseases fitting and forecasting in Jingzhou City than the SARIMA model.