CAJ | 학술논문

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。
이용기이보분석방법대고시시간서렬중구，강저조성병제취추세서렬，병이용C-C산법학정감입위유수화연지계수진행상공간중구，생성신경망락적학습구진，진일보이용Boosting기술화불동적신경망락모형，생성신경망락집성개체，최후채용비삼수회귀모형진행집성，건립다원변창관고사핵함수적비삼수회귀적신경망락집성모형，이차건립고시예측모형．통과S＆P500지수개반개진행실례분석，여전통적시간서렬분석화기타집성방법대비，발현해방법능획득경준학적예측결과．계산결과표명해방법능충분반영고표개격시간서렬추세，위금융시간서렬예측제공일개유효방법。
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.