New optimized grey derivative models for grain production forecasting in China

Abstract

SUMMARYAlthough the grey forecasting model has been successfully employed in various fields and demonstrates promising results, the literature shows that its performance could still be improved. Therefore, the aim of the present study was to continue the investigation and derive three hybrid models to predict grain production in China by combining particle swarm optimization (PSO) with the grey linear power index model, the grey logarithm power model and the grey parabola power model. In grey modelling, the use of PSO had the ability to search optimum grey parameters to construct three improved derivative grey models. The results concluded that the improved optimization models with high precision were superior to the traditional models, and PSO contributed more to precision improvement of the three grey models. Furthermore, results from the experiments demonstrated that the optimized models were reliable and valid.