[1]吴朝阳.改进的灰色模型与ARMA模型的股指预测[J].智能系统学报,2010,5(3):277-281.
WU Zhao-yang.Forecasting stock indexes based on a revised grey model and the ARMA model[J].CAAI Transactions on Intelligent Systems,2010,5(3):277-281.
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《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
5
期数:
2010年第3期
页码:
277-281
栏目:
学术论文—智能系统
出版日期:
2010-06-25
- Title:
-
Forecasting stock indexes based on a revised grey model and the ARMA model
- 文章编号:
-
1673-4785(2010)03-0277-05
- 作者:
-
吴朝阳
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康考迪亚大学 统计与数学系,蒙特利尔 H3G 2H9
- Author(s):
-
WU Zhao-yang
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The Department of Mathematics and Statistics, Concordia University, Montreal H3G 2H9, Canada
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- 关键词:
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灰色模型; GM(1; 1)模型; ARMA模型; GMARMA模型; 股指预测
- Keywords:
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grey model; GM (1; 1) model; ARMA model; GMARMA model; stock prediction
- 分类号:
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TP18
- 文献标志码:
-
A
- 摘要:
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当前基于灰色GM(1,1)模型和ARMA模型的组合模型GMARMA模型存在着2点不足:一是由于GM(1,1)模型不是最优的,导致了GMARMA模型也不是最优的;二是GMARMA模型并没有恰当地结合2个子模型,这也导致了GMARMA模型不是最优的. 为此,首先引入数据维度参数和白化背景值的系数2个参数来改进GM(1,1)模型,然后同时优化ARMA模型中的P、Q 2个参数来改进GMARMA模型, 称新的模型为Revised GMARMA(RGMARMA)模型.实例证明RGMARMA的误差小于ARIMA和GMARMA模型,并且为组合模型的建立提供了新的思路.
- Abstract:
-
A hybrid grey model—autoregressive moving average (GMARMA) model, constructed by combing the GM (1, 1) model and the ARMA model, has two drawbacks. One drawback is that the GMARMA model may not be optimal since the traditional GM (1, 1) model is not optimal. The other is that the GMARMA model does not combine two submodels properly; this may also cause the GMARMA model to be suboptimal. This paper tries to first modify the GM (1, 1) model by introducing 2 parameters, the grey dimension degree and white background value. A revised GMARMA model was constructed by optimizing all parameters in the GM (1, 1) model and the ARMA model simultaneously. For convenience, we called this revised GMARMA model the RGMARMA model. Experimental results showed that the RGMARMA model has fewer prediction errors than the ARMA model or the GMARMA model and gives a new solution for construction of hybrid models.
更新日期/Last Update:
2010-08-27