关键词: 贝叶斯正则化, 神经网络, 股票时间序列预测

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 network 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 traditional BP model.

Key words: Bayesian Regularization（BR）, neural network, stock time series prediction