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The exponential grey forecasting model for CO2 emissions in Taiwan

Chen-Fang Tsai (Department of Industrial Management and Enterprise Information, Aletheia University, New Taipei City, Taiwan)
Shin-Li Lu (Department of Industrial Management and Enterprise Information, Aletheia University, New Taipei City, Taiwan)

Grey Systems: Theory and Application

ISSN: 2043-9377

Article publication date: 2 November 2015

147

Abstract

Purpose

The purpose of this paper is to improve the forecasting efficiency of a grey model.

Design/methodology/approach

The exponentially weighted moving average (EWMA) algorithm is proposed to modify background values for a new grey model optimization.

Findings

The experimental results reveal that the proposed models (EGM, REGM) outperform traditional grey models.

Originality/value

A genetic algorithm (GA) optimizer is used to select the optimal weights for the background values of the EGM(1,1) and REGM(1,1) forecast models. The results of the current study are very encouraging, as the empirical results show that the REGM(1,1) and EGM(1,1) models reduce the MAPE rates over the traditional GM(1,1) and RGM(1,1) models.

Keywords

Citation

Tsai, C.-F. and Lu, S.-L. (2015), "The exponential grey forecasting model for CO2 emissions in Taiwan", Grey Systems: Theory and Application, Vol. 5 No. 3, pp. 354-366. https://doi.org/10.1108/GS-05-2015-0026

Publisher

:

Emerald Group Publishing Limited

Copyright © 2015, Emerald Group Publishing Limited

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