A novel grey prediction model with structure variability and real domain fractional order and its performance comparisons

Abstract

PurposeThis paper aims to overcome the problem that the single structure of the driving term of the grey prediction model is not adapted to the complexity and diversity of the actual modeling objects, which leads to poor modeling results.Design/methodology/approachFirstly, the nonlinear law between the raw data and time point is fully mined by expanding the nonlinear term and the range of order. Secondly, through the synchronous optimization of model structure and parameter, the dynamic adjustment of the model with the change of the modeled object is realized. Finally, the objective optimization of nonlinear driving term and cumulative order of the model is realized by particle swarm optimization PSO algorithm.FindingsThe model can achieve strong compatibility with multiple existing models through parameter transformation. The synchronous optimization of model structure and parameter has a significant improvement over the single optimization method. The new model has a wide range of applications and strong modeling capabilities.Originality/valueA novel grey prediction model with structure variability and optimizing parameter synchronization is proposed.HighlightsThe highlights of the paper are as follows:A new grey prediction model with a unified nonlinear structure is proposed.The new model can be fully compatible with multiple traditional grey models.The new model solves the defect of poor adaptability of the traditional grey models.The parameters of the new model are optimized by PSO algorithm.Cases verify that the new model outperforms other models significantly.