基于引力搜索算法的分数阶变异时序回归GSA-TSGM(1,1)模型

Abstract: In order to use the high performance of fractional accumulated generating operator (FAGO) in the short-term grey prediction, in this paper, it firstly added FAGO in the time sequence variation grey model TSGM(1,1) to get higher accuracy. The main method organized as follow. Firstly, use the data of 1978 to 1987 from the monitoring station of “Lianziya” mountain in Hubei province as training data to optimal FAGO by using gravitational search algorithm and then use the data of 1988-1993 as verifying data to test the accuracy of the proposed grey model. Secondly, it compared other grey model GM(1,1) , fractional accumulated generating GM(1,1) and TSGM(1,1) . The result was that as follow. Firstly, corrected the error in the Chen's article. Moreover, it showed that the proposed model in this paper has higher prediction accuracy. Therefore, the novel model improves accuracy of the grey theory in long-term prediction by fractional accumulated generating operator and it provides the guide in the long-term prediction.