摘要
为实现对非平稳、非线性股票价格时间序列的高精度预测,提出经验模态分解下基于支持向量回归的股票价格集成预测方法EMD-SVRF(EMD and SVR based stock price integrated forecasting)。首先,运用经验模态分解方法获得股票对数收益率时间序列的本征模函数及趋势序列,然后,利用ε不敏感支持向量回归为各本征模函数及趋势序列分别建立预测模型,并计算各本征模函数及趋势项的预测值,最后,集成得到股票收益率序列预测值。实验表明,相对现有的EMD-Elman网络和ARMA-GARCH等主流股价预测方法,EMD-SVRF具有更小的拟合误差和预测误差,是一种高精度的股票价格预测方法。
In order to achieve high-precision prediction of non-stationary and non-linear stock price time series,an integrated stock price forecasting method EMD-SVRF based on empirical mode decomposition and support vector regression is proposed.Firstly,the empirical mode decomposition method is used to extract intrinsic mode functions and trend term from stock logarithmic return time series.Then,several prediction models are established for intrinsic modulus function and trend subsequence withε-insensitive support vector regression applied,and the predicted values of each intrinsic mode function and trend term are calculated.Finally,these predicted values are integrated to be the predicted value of stock return.Experimental results demonstrate that EMD-SVRF is a high-precision stock price prediction method since it has smaller fitting and prediction error than other mainstream methods such as EMD-Elman network and ARMA-GARCH.
作者
贺毅岳
高妮
王峰虎
茹少峰
韩进博
HE Yiyue;GAO Ni;WANG Fenghu;RU Shaofeng;HAN Jinbo(School of Economics & Management, Northwest University, Xi′an 710127, China;School of Information, Xi′an University of Finance and Economics, Xi′an 710100, China)
出处
《西北大学学报（自然科学版）》
CAS
CSCD
北大核心
2019年第3期329-336,共8页
Journal of Northwest University（Natural Science Edition）
基金
教育部人文社会科学研究青年基金资助项目(16XJC630001)
中国博士后科学基金面上项目(2017M623229)
陕西省自然科学基础研究计划资助项目(2015JQ7278)
陕西省教育厅科研计划资助项目(17JK0304)
国家自然科学基金资助项目(71701162)
关键词
股票价格
时间序列建模
集成预测
经验模态分解
支持向量回归
stock price
time series modeling
integrated forecasting
empirical mode decomposition
support vector regression
