摘要
提出了一种基于小波变换与改进动量BP神经网络(MOBP)的股价预测方法。将股票价格所构成的非平稳时间序列小波分解,建立基于优化权值的改进动量BP神经网络(MOBP)预测模型,对分解得到的近似部分与各细节部分分别进行训练,结合各部分的预测结果,可以得到原始序列的预测值。实验结果表明,这种方法预测效果较为理想,且相对于传统的BP神经网络预测的准确度有明显的提高。
This paper proposes the forecasting method based on wavelet transform and MOBP.By using the function of wavelet transform,the non-stationary time series of the stoke price can be decomposed into approximate series and several detail series, then build the forecasting model on MOBP,carry out the training of MOBP.The prediction of the original series can be obtained by the synthesis of prediction result of each series.The experiment results show that the forecasting performance on this method is good,and can obtain the higher forecastin precision than the conventional BP neural network.
出处
《计算机工程与应用》
CSCD
北大核心
2008年第16期215-217,共3页
Computer Engineering and Applications
基金
国家高技术研究发展计划(863)(the National High-Tech Research and Development Plan of China under Grant No.2006AA01A116)
广东省自然科学基金(the Natural Science Foundation of Guangdong Province of China under Grant No.0610576)
深圳市科技计划项目(No.200511)
