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An Application of Genetic Programming to Economic Forecasting

In: Current Trends in High Performance Computing and Its Applications

Author

Listed:
  • Kangshun Li

    (Wuhan University, State Key Laboratory of Software Engineering
    Jiangxi University of Science & Technology, School of Information Engineering)

  • Zhangxin Chen

    (Southern Methodist University, Center for Scientific Computation and Department of Mathematics)

  • Yuanxiang Li

    (Wuhan University, State Key Laboratory of Software Engineering)

  • Aimin Zhou

    (Wuhan University, State Key Laboratory of Software Engineering)

Abstract

In this paper, we propose an application of genetic programming to economic forecasting that can obviously improve traditional economic forecasting methods; the latter can only obtain rough fitting curves with unsatisfactory results. Forecasted and estimated standard errors are also computed and analyzed. Using practical historical data from Statistical Yearbooks of the People’s Republic of China in recent years, an automatically generated mathematical model of economic forecasting by genetic programming is established. Forecasting results indicate that the accuracy obtained by genetic programming is obviously higher than traditional methods such as linear, exponential, and parabolic regression methods.

Suggested Citation

  • Kangshun Li & Zhangxin Chen & Yuanxiang Li & Aimin Zhou, 2005. "An Application of Genetic Programming to Economic Forecasting," Springer Books, in: Wu Zhang & Weiqin Tong & Zhangxin Chen & Roland Glowinski (ed.), Current Trends in High Performance Computing and Its Applications, pages 71-80, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27912-9_7
    DOI: 10.1007/3-540-27912-1_7
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