基于EMD与神经网络的中国股票市场预测

Prediction of China stock market based on EMD and neural network

应用EMD分解算法、混沌分析和神经网络理论提出了一种中国股票市场建模及预测的EMD神经网络模型.首先应用EMD分解算法把原始股市时间序列分解成不同尺度的基本模态分量,并在此基础上进一步分析,表明中国股市存在混沌特性;再经混沌分析和神经网络进行组合预测,提高了模型对多种目标函数的学习能力,有效提高了预测精度.实验表明:与现有方法相比,该方法具有较高的精度.

Following empirical mode decomposition（EMD）,chaos analysis and neural network theory,a method is presented to model and forecast stock market.First,using EMD theory,the stock market time serial is decomposed into many intrinsic modal functions（IMF） which can significantly represent potential information of original time serial,and the further analysis of IMF indicates that China stock market exists a chaos feature.Then,by using chaos theory and neural network,the forecasting models are established to forecast the IMF respectively.By these means,the model can be improved to learn various objective function and more precious prediction can be obtained.The experiments show that the presented method can effectively improve the prediction accuracy.