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Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China


  • Zhao, Ze
  • Wang, Jianzhou
  • Zhao, Jing
  • Su, Zhongyue


China is a major developing country where farmers account for over 57% of the population. Thus, promoting a rural economy is crucial if the Chinese government is to improve the quality of life of the nation as a whole. To frame scientific and effective rural policy or economic plans, it is useful and necessary for the government to predict the income of rural households. However, making such a prediction is challenging because rural households income is influenced by many factors, such as natural disasters. Based on the Grey Theory and the Differential Evolution (DE) algorithm, this study first developed a high-precision hybrid model, DE–GM(1,1) to forecast the per capita annual net income of rural households in China. By applying the DE algorithm to the optimization of the parameter λ, which was generally set equal to 0.5 in GM(1,1), we obtained more accurate forecasting results. Furthermore, the DE–Rolling–GM(1,1) was constructed by introducing the Rolling Mechanism. By analyzing the historical data of per capita annual net income of rural households in China from 1991 to 2008, we found that DE–Rolling–GM(1,1) can significantly improve the prediction precision when compared to traditional models.

Suggested Citation

  • Zhao, Ze & Wang, Jianzhou & Zhao, Jing & Su, Zhongyue, 2012. "Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China," Omega, Elsevier, vol. 40(5), pages 525-532.
  • Handle: RePEc:eee:jomega:v:40:y:2012:i:5:p:525-532 DOI: 10.1016/

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    Cited by:

    1. repec:eee:apmaco:v:265:y:2015:i:c:p:400-408 is not listed on IDEAS
    2. Li, Guo-Dong & Masuda, Shiro & Nagai, Masatake, 2014. "Predicting the subscribers of fixed-line and cellular phone in Japan by a novel prediction model," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 321-330.
    3. Xu, Ning & Dang, Yaoguo & Gong, Yande, 2017. "Novel grey prediction model with nonlinear optimized time response method for forecasting of electricity consumption in China," Energy, Elsevier, vol. 118(C), pages 473-480.
    4. Wang, Jianzhou & Jiang, Haiyan & Zhou, Qingping & Wu, Jie & Qin, Shanshan, 2016. "China’s natural gas production and consumption analysis based on the multicycle Hubbert model and rolling Grey model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1149-1167.
    5. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
    6. repec:eee:touman:v:52:y:2016:i:c:p:369-379 is not listed on IDEAS
    7. Chen, Lin & Lin, Weilong & Li, Junzi & Tian, Binbin & Pan, Haihong, 2016. "Prediction of lithium-ion battery capacity with metabolic grey model," Energy, Elsevier, vol. 106(C), pages 662-672.
    8. repec:eee:energy:v:132:y:2017:i:c:p:269-279 is not listed on IDEAS
    9. Ma, Weimin & Zhu, Xiaoxi & Wang, Miaomiao, 2013. "Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm," Resources Policy, Elsevier, vol. 38(4), pages 613-620.
    10. Lwin, Khin T. & Qu, Rong & MacCarthy, Bart L., 2017. "Mean-VaR portfolio optimization: A nonparametric approach," European Journal of Operational Research, Elsevier, vol. 260(2), pages 751-766.


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