A new grey intelligent prediction algorithm with multiobjective correction strategy

Abstract

As a combination of the differential equation prediction model and intelligent optimization algorithm, the grey intelligent prediction algorithm has attracted increasing attention due to its outstanding performance in small-sample environments. However, most studies focus only on the improvement of algorithm performance, with little regard to the uniformity, ill-condition and overfitting of the algorithm. To promote the development of this field, we develop a new grey intelligent prediction algorithm with multiobjective correction strategy based on the new weakened accumulation grey optimization model and the multiobjective grey wolf optimizer. In this new prediction algorithm, the new weakened accumulation operation is utilized to enhance the predictive ability of the algorithm and mitigate the ill-condition of the system, the Bernoulli parameter and a discretization technique are used to activate the uniformity and unbiasedness of the algorithm, respectively, and the multiobjective grey wolf optimizer is employed to alleviate the overfitting of the system. Compared with the previous grey intelligent prediction models, the new prediction algorithm is more perfect and reasonable. Taking two energy datasets as research cases, the evaluation results of a system consisting of six evaluation metrics and the Diebold-Mariano test show that the proposed model outperforms the other six comparative models in terms of prediction performance and stability, which confirms the feasibility and validity of the algorithm.