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.
This work is partly supported by the National Natural Science Key Foundation of China with the Grant No.60133010 and the National Research Foundation for the Doctoral Program of Higher Education of China with the Grant No.20030486049.
This work is partly supported by the National Natural Science Key Foundation of China with the Grant No.60133010 and the National Research Foundation for the Doctoral Program of Higher Education of China with the Grant No.20030486049.
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Li, K., Chen, Z., Li, Y., Zhou, A. (2005). An Application of Genetic Programming to Economic Forecasting. In: Zhang, W., Tong, W., Chen, Z., Glowinski, R. (eds) Current Trends in High Performance Computing and Its Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27912-1_7
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DOI: https://doi.org/10.1007/3-540-27912-1_7
Publisher Name: Springer, Berlin, Heidelberg
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