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An optimized grey dynamic model for forecasting the output of high-tech industry in China. (English) Zbl 1407.90217

Summary: The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data and \(n\) series related, abbreviated as GDMC\((1, n)\), performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC\((1, n)\), \(n\) interpolation coefficients (taken as unknown parameters) are introduced into the background values of the \(n\) variables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC\((1, n)\) model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC\((1, n)\) model. The modelling results can assist the government in developing future policies regarding high-tech industry management.

MSC:

90B50 Management decision making, including multiple objectives
62P30 Applications of statistics in engineering and industry; control charts
91B84 Economic time series analysis
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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References:

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