基于EEMD-Elman-Adaboost的中美股票价格预测研究

针对股票价格序列高度非正态、非线性、非平稳等复杂特征,文章以Elman神经网络为基础,引入集合经验模态分解(EEMD)与Adaboost算法,对中美股票的日收盘价进行预测。首先,利用EEMD算法将样本分解为多个本征模函数分量和1个残差分量。其次,用Adaboost算法优化Elman神经网络,对各个分量进行预测。最后,将各分量预测结果进行求和,作为最终预测结果。研究结果表明:EEMD-Elman-Adaboost模型对中美股票价格预测的均方根误差、平均相对误差、平均绝对误差均比现有的BP、Elman、EMD-Elman、EEMD-Elman模型小,新组合模型融合了EEMD、Elman神经网络、Adaboost算法的优点,具有更强的泛化能力和跟随能力。

Research on Stock Price Prediction of China and America Based on EEMD-Elman-Adaboost

Aiming at the complex features such as highly non-normal, non-linear, non-stationary characteristics of stock price series, this paper introduces ensemble empirical mode decomposition(EEMD) and Adaboost algorithm based on Elman neural network to predict the daily closing price of Chinese and American stocks. Firstly, the EEMD algorithm is used to decompose the sample data into several intrinsic mode function(IMF) components and a residual component. Secondly, the Elman neural network is optimized with the Adaboost algorithm to make rolling predictions for each component. Finally, the sum of the prediction results of each component is used as the final prediction result. The results show that the root mean square error, mean absolute percentage error, and mean absolute error of EEMD-Elman-Adaboost model for predicting the stock prices of China and America are smaller than those of existing BP, Elman, EMD-Elman and EEMD-Elman models. The new combination model integrates the advantages of EEMD, Elman neural network and Adaboost algorithm, so that it has stronger generalization ability and following ability.