关键词: 股票收盘价, EEMD, Elman, Adaboost, 组合模型预测

Abstract: 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.

Key words: stock closing price, EEMD, Elman, Adaboost, combination model prediction