A Forecasting Model With Variable Weight Combination Based On GRNN For Infectious Diseases

The numerous influence Factors of infectious diseases has complex relationships with each others, so there are various kinds of forecasting model for infectious diseases. There are two kinds of forecasting model:one kind of them are called simple prediction model such as linear regression model, time series models, the gray models, artificial neural network models and so on; the others are called combination forecast model which are comprised of more than one kind of simple prediction models with different ways. The combination forecast model can be divided into fixed weight combination forecasting model and the variable weight combination forecasting model.ObjectiveA forecasting model with variable weight combination based on GRNN was built with the time series models and the gray models for for Infectious Diseases. To provide evidence for variable weight combination, these forecasting models was used to predict the incidence rate of the diseases and evaluated to study the advantages and the weakness of them. Data and MethodsThe monthly incidence rate of tuberculosis between 1998 and 2008 were collected from the Center for Disease Control and Prevention in yiwu.With these data, the grey models and the time series models were built with the software called matlab 7.11.0 and SAS 9.2, respectively. Then these models were used to predict he monthly incidence rate of tuberculosis and evaluated which one is better.A forecasting model with variable weight combination based on GRNN for infectious diseases was made up of the grey models and the time series models in the matlab7.11.0. A simple combination forecasting model and a weighted average combined forecasting model were made up of the grey models and the time series models, respectively. The estimation accuracy of the forecasting model based on GRNN was evaluated by comparing with the other two combination forecasting models.Results1. The grey modelsA GM(1,1) model and a residual-modifying GM(1,1) model were built with the monthly incidence rate of tuberculosis between 1998 and 2008 of yiwu. The value of posterior-variance-test of the GM(1,1) model and the residual-modifying GM(1,1) model was 0.7687,0.4140,respectively. The evaluation index of the residual-modifying GM(1,1) model were smaller than the evaluation index of the GM(1,1) model, so the former have better estimation accuracy.2. The ARIMA modelsAn ARIMA(1,0,0) model and an ARIMA(1,O,1)* (1,1,0)12 model were built with the monthly incidence rate of tuberculosis between 1998 and 2008 of yiwu. The white noise test shows:the residual value of the ARIMA(1,0,1)* (1,1,0)12 model was a white noise sequence after 12 lags, but the residual value of the ARIMA(1,0,0) model was not a white noise sequence after 12 lags. The AIC value and the SBC value of former model was 587.4054,595.7679, The AIC value and the SBC value of later model was 587.4054,595.7679, respectively. The evaluation index of the ARIMA(1,0,1)* (1,1,0)12 model were smaller than the ARIMA(1,0,0) model, so the former have better estimation accuracy.3. The combination forecasting modelA forecasting model with variable weight combination based on GRNN for monthly incidence rate of tuberculosis was made up of the grey models and the ARIMA models. A simple combination forecasting model and a weighted average combined forecasting model were made up of the two simple models. The combination forecasting model based on GRNN was compared with the other four models by comparing the MSE value, the MAE value, the MAPE value and the MER value, the results were as follows:the four values of the residual-modifying GM(1,1) model was 37.451,5.692,53.69%,48.51%, respectively; the four values of the ARIMA(1,0,1)* (1,1,0)12 model was 18.509,3.761,35.13%,32.05%, respectively; the four values of the simple combination forecasting model was 28.984,4.736,45.4%,40.4%, the four values of the weighted average combined forecasting model was 24.649,4.274,41.0%, 36.4%, respectively; the four values of the combination forecasting model based on GRNN was 9.961,2.571,25.6%,21.9%, respectively. The evaluation index of the models showed that the values of the five models are the combination forecasting model based on GRNN< the ARIMA(1,0,1)*(1,1,0)12 model< the weighted average combined forecasting model