计算机工程与应用2019，Vol.55Issue(12)：225-229,1,6.DOI:10.3778/j.issn.1002-8331.1803-0400
贝叶斯神经网络在股票时间序列预测中的应用
Application of Bayesian Neural Network in Prediction of Stock Time Series
摘要
Abstract
In view of the problem of being apt to fall into local optimum caused by stochastic acquisition of initial weights of BP neural network, Bayesian Regularization(BR)algorithm is used to improve the traditional BP neural net-work model. The algorithm can adjust the fitness function of the BP model through the prior probability of historical data on the premise of ensuring the minimum network error, so that the generalization ability of the network is improved. The empirical study on stock time series forecasting shows that the prediction accuracy is 42.81% higher than that of the tradi-tional BP model.关键词
贝叶斯正则化/神经网络/股票时间序列预测Key words
Bayesian Regularization(BR)/ neural network/ stock time series prediction分类
信息技术与安全科学引用本文复制引用
刘恒,侯越..贝叶斯神经网络在股票时间序列预测中的应用[J].计算机工程与应用,2019,55(12):225-229,1,6.基金项目
甘肃省教育厅项目(No.2016B-027). （No.2016B-027）
