| 基于GM(1,1,μ,ν)模型的股指预测 |
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| 引用本文：吴朝阳.基于GM(1,1,μ,ν)模型的股指预测[J].计算技术与自动化,2010,(3):113-116 |
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| 中文摘要:当前对灰色预测模型GM(1,1)的优化主要集中在2个方面，1个是建模所用数据维度的选择上，一个集中在白化背景值z(1)(k)的优化上.由于这些工作都只考虑单一因素的影响，因此，GM(1,1)的潜力还没有被充分的挖掘出来. 针对以上情况，提出同时考虑2个因素的GM(1,1,μ,ν)模型，并根据股市的特点，提出选择最优解的办法. 实例证明该模型比传统的GM(1,1)有更低的预测误差. 更重要的是，该模型提出综合考虑2种因素的思想，为更进一步优化GM(1,1)提供新的思路. |
| 中文关键词:灰色模型 GM(1,1)模型 股指预测 |
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| Forecasting Stock Index Based on the GM(1,1,μ,ν) Model |
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| Abstract:Usually, researchers optimize a transitional GM (1, 1) model by two factors. One factor is the data dimension used to set up a GM (1, 1) model. The other factor is a coefficient used to set for the background valuez(1)(k). Since all the work only counts one factor, the potential of the GM (1, 1) model is far from digging out. In this paper, we propose theGM(1,1,μ,ν) model by considering the two factors simultaneously, and also provide a way to find the optimal solution. Experimental results show that the GM GM(1,1,μ,ν) model has lower prediction error than the traditional GM (1, 1) model. More important, it brings a new idea to optimize the GM (1, 1) model, which is we should optimize the GM (1, 1) model by counting the two factors at the same time. |
| keywords:the grey model the GM (1, 1) model stock prediction |
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