An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting

Yi-Chung Hu, Peng Jiang, Jung-Fa Tsai and Ching-Ying Yu
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Yi-Chung Hu: College of Management & College of Tourism, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Peng Jiang: School of Business, Shandong University, Weihai 264209, China
Jung-Fa Tsai: Department of Business Management, National Taipei University of Technology, Taipei 10608, Taiwan
Ching-Ying Yu: College of Management, Yuan Ze University, Taoyuan 32003, Taiwan

IJERPH, 2021, vol. 18, issue 2, 1-12

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 fractional-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.

Keywords: genetic algorithm; grey theory; forecasting; fractional-order; carbon dioxide emissions (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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