The Combined Forecasting Method of GM(1,1) with Linear Regression And Its Application

摘要：

Linear regression analysis could get better results in a short-term forecast. However, when some aberrant points exist in a given raw data sequence, it will be difficult for the linear regression function to accurately predict the changing tendency of the data sequence. To solve the problem, firstly, the raw data sequence with some abnormal data is classified into two parts: aberrant data and normal data; then, applying the principle of grey disaster, we can make use of GM (1,1) to forecast the possible aberrant date points in the future based on the aberrant data, and for other normal data points, linear regression function can be applied to get a forecast value. By applying the combined method to the prediction of the Gross Domestic Production of Henan province, it showed that the new method could achieve better forecasting results compared with other forecasting models, and make up for some deficiencies in GM (1,1) model and linear regression model in a sense.

作者: Li Bing-jun He Chun-hua

作者单位: College of Information and Management Science, Henan Agricultural University, Zhengzhou, 450002, China

会议类型: 国际会议

会议名称: 2007年IEEE灰色系统与智能服务国际会议(2007 IEEE International Conference on Grey Systems and Intelligent Services)

会议地点: 南京

会议语种:英文

在线出版日期: 2007-11-18（万方平台首次上网日期，不代表论文的发表时间）