基于相空间重构和非参数神经网络集成股市预测研究  

Stock Market Prediction Model Using Neural Network Ensemble Based on SSA and NR

作　　者：赵世安[1] 

机构地区：[1]百色学院数学与计算机信息工程系,广西百色533000

出　　处：《广西民族大学学报（自然科学版）》2011年第3期54-59,共6页Journal of Guangxi Minzu University ：Natural Science Edition

基　　金：广西青年科学基金(0832092);广西教育厅面上项目(200707MS061)

摘　　要：利用奇异谱分析方法对股市时间序列重构,降低噪声并提取趋势序列,并利用C-C算法确定嵌入为维数和延迟阶数进行相空间重构,生成神经网络的学习矩阵,进一步利用Boosting技术和不同的神经网络模型,生成神经网络集成个体,最后采用非参数回归模型进行集成,建立多元变窗宽高斯核函数的非参数回归的神经网络集成模型,以此建立股市预测模型.通过S&P500指数开盘价进行实例分析,与传统的时间序列分析和其他集成方法对比,发现该方法能获得更准确的预测结果.计算结果表明该方法能充分反映股票价格时间序列趋势,为金融时间序列预测提供一个有效方法.A novel neural network ensemble model is proposed for stock market prediction. First of all, the original data of time series are reconstructed for reduction the noise and extraction the tendency by Singular Spectrum Analysis （SSA）. Secondly, C-C algorithm are adopted to confirm the best delay time and the best embedding dimension for phase space reconstruction, and the learning matrix can be obtained. Third, many individual neural networks are generated by Bagging techniques and different model of neural network. Finally, the nonparametric regression （NR） model is used to neural network ensemble based on Gaussian kernel function estimation with variable band-with. This method be established the forecast model of Stock Market by the opening price of S＆P 500 index as an example, more accurate results can be acquired by this method compared with the traditional time series analysis and other integrated methods. The result shows the way have high accuracy, and it is a useful tool for the stock market forecasting.

关 键 词：奇异谱分析 相空间重构 神经网络集成 非参数回归 

分 类 号：F832.0[经济管理—金融学]