A self-adaptive grey forecasting model and its application  

作　　者：TANG Xiaozhong XIE Naiming 

机构地区：[1]College of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China [2]Department of Industry and Finance,Huangshan Vocational and Technical College,Huangshan 245000,China

出　　处：《Journal of Systems Engineering and Electronics》2022年第3期665-673,共9页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(72171116,71671090);the Fundamental Research Funds for the Central Universities(NP2020022);the Key Research Projects of Humanities and Social Sciences in Anhui Education Department(SK2021A1018);Qinglan Project for Excellent Youth or Middlea ged Academic Leaders in Jiangsu Province,China.

摘　　要：GM(1,1)models have been widely used in various fields due to their high performance in time series prediction.However,some hypotheses of the existing GM(1,1)model family may reduce their prediction performance in some cases.To solve this problem,this paper proposes a self-adaptive GM(1,1)model,termed as SAGM(1,1)model,which aims to solve the defects of the existing GM(1,1)model family by deleting their modeling hypothesis.Moreover,a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed,the proposed multi-parameter optimization scheme adopts machine learning ideas,takes all adjustable parameters of SAGM(1,1)model as input variables,and trains it with firefly algorithm.And Sobol’sensitivity indices are applied to study global sensitivity of SAGM(1,1)model parameters,which provides an important reference for model parameter calibration.Finally,forecasting capability of SAGM(1,1)model is illustrated by Anhui electricity consumption dataset.Results show that prediction accuracy of SAGM(1,1)model is significantly better than other models,and it is shown that the proposed approach enhances the prediction performance of GM(1,1)model significantly.

关 键 词：grey forecasting model GM(1 1)model firefly algo-rithm Sobol’sensitivity indices electricity consumption prediction 

分 类 号：N941.5[自然科学总论—系统科学]