摘要
股票数据具有非线性和含有大量噪声的特点,传统股票预测模型难以充分识别股票非线性特征以及降低噪声,导致预测精度不高.为了提高预测精度,去除冗余特征并加强特征的区分度,引入流形学习中的线性局部切空间排列算法,提出了一种新的支持向量回归机的股价预测优化模型.首先利用线性局部切空间排列算法对股票原始数据进行特征提取,然后采用支持向量回归机对提取到的特征和股票价格之间的非线性关系建模,并利用遗传算法优化支持向量回归机的参数,最终提高股票价格的预测精度.为证明模型的有效性,采用标准普尔500指数在2012—2013年、2014—2015年2个时间段内的股票数据进行检验.实验证明,提出的模型相较其他对比模型具有更高的预测精度,更强的泛化能力.
Stock data has the characteristics of nonlinearity and lots of noise. Traditional stock prediction models cannot sufficiently recognize the nonlinear features and reduce noise in stock data, which leads to low prediction accuracy.In order to improve the prediction accuracy, remove redundant attributes and enhance discriminability of features, this study cites linear local tangent space alignment from manifold learning and proposes a novel stock price prediction model based on optimized support vector regression.Firstly ,linear local tangent space alignment algorithm is used to extract features of original stock data.Then support vector regression is utilized to model the nonlinear relationship between those features and stock close price. Meantime genetic algorithm is used to optimize parameters of support vector regression.Finally ,the prediction accuracy is improved.To validate the ef- fectiveness of the model,S＆P 500 index stock data from the year 2012 to 2013,and the year 2014 to 2015 is a- dopted.The experiments prove that the proposed model outperforms others and has higher accuracy and more powerful generalization ability.
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2016年第4期536-542,共7页
Journal of Yunnan University(Natural Sciences Edition)
基金
国家自然科学基金(61379158)
关键词
股票预测
遗传算法优化
线性局部切空间排列
支持向量回归机
stock price prediction
genetic algorithms
linear local tangent space alignment
support vector regression
