An optimized fractional grey prediction model for carbon dioxide emissions forecasting

Abstract

Because grey prediction does not demand that the collected data have to be in line with any statistical distribution, it is pertinent to set up grey prediction models for real-world problems. GM(1,1) has been a widely used grey prediction model, but relevant parameters, including the control variable and developing coefficient, rely on background values that are not easily determined. Furthermore, one-order accumulation is usually incorporated into grey prediction models, which assigns equal weights to each sample, to recognize regularities embedded in data sequences. Therefore, to optimize grey prediction models, this study employed a genetic algorithm to determine the relevant parameters and assigned appropriate weights to the sample data using fraction-al-order accumulation. Experimental results on the carbon dioxide emission data reported by the International Energy Agency demonstrated that the proposed grey prediction model was significantly superior to the other considered prediction models.