| 摘要: |
| 针对GDP未来一段时间的变化范围及走势进行预测，提出了信息粒化算法与支持向量回归机算法相结合的时间序列组合预测模型；以1994年第1季度到2013第4季度GDP值模糊粒化处理，对3个模糊参数Low、R、Up，通过交叉实验训练寻找支持向量回归机的最优参数，并进行回归预测，得出GDP在2014年各季度的变化范围与走势，与实际比较相吻合，说明算法具有良好的泛化性，能够作为时序数据的预测模型。 |
| 关键词: 信息粒化 支持向量回归机 GDP |
| DOI： |
| 分类号: |
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| Application of SVR Combined Model with Information Granulation to Quarterly GDP |
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ZHANG Peng
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| Abstract: |
| In order to predict GDP change and trend in a period of future, this paper proposes time series combination forecast model based on the combination of information granulation algorithm and support vector regression machine algorithm, makes fuzzy granulation processing on the GDP values from the first quarter of 1994 to the forth quarter of 2013, uses cross over experiment training to seek the optimal parameter of support vector regression machine and conducts regression forecast based on three fuzzy parameters such as Low, R and Up and obtains GDP change and trend in each quarter of 2014 which is identical to the practice. The results show that this algorithm has good generalization and can be used as prediction model for time series data. |
| Key words: information granulation support vector regression machine GDP |
