Hybrid forecasting method of GM(1,1) disaster model with application to regional ggain production

Each technique has its own drawback and advantage. There is no method that is powerful in any problems. Therefore, the hybridization of two or more different techniques is important to overcome the disadvantages of the individual techniques. In this paper, a data sequence having a linear tendency with upper/positive and lower/negative aberrances is analyzed. Based on the linear regression analysis, the data sequence is classified into three parts: upper/positive aberrant data, lower/negative aberrant data and normal data. Then introducing the grey disaster forecast analysis, we establish three models: a GM(1,1) disaster model based on upper aberrant data, a GM(1,1) disaster model based on lower aberrant data and a linear regression model based on the remaining normal data. Using the established models, we obtain aberrant forecasting values at oncoming aberrant time points by GM(1,1) from upper and lower aberrant data, and normal forecasting value obtained by the linear regression function. Applying it to the prediction of regional grain production, we demonstrate the good performance and effectiveness of the proposed hybrid method.