一种股票市场的深度学习复合预测模型

doi:10.11896/jsjkx.200500119

Abstract

Abstract: Without relying on prior knowledge,deep learning can extract high-level abstract features from a large amount of raw data,which is potentially attractive for financial market forecasting.Based on the idea of “Decomposition-Reconstruction-Integration”,this paper proposes a new method of deep learning prediction methodology,and constructs a deep learning hybrid prediction model－CEEMD-LSTM－for forecasting one-step-ahead closing price of stock market.In this model,CEEMD,as a sequence smoothing decomposition module,can decompose the fluctuations or trends of different scales in time series step by step,producing a series of Intrinsic Mode functions (IMFs) with different feature scales.Long and short-term memory network (LSTM),which is suitable for processing time series in deep learning,is adopted to extract advanced and deep features of each IMF and residual term and to predict the return of closing price of the next trading day.Finally,the predicted values of each IMF components and residual term are integrated to obtain the final predicted value.The empirical results of the stock indices from three stock markets of different developed types demonstrates that the proposed model is superior to other benchmark models in two dimensions－predictive error (RMSE,MAE,NMSE) and predicted directional symmetry (DS).

Key words: Complementary ensemble empirical mode decomposition, Deep learning, Long-short term memory, Prediction methodology based on deep learning, Stock market forecasting

CLC Number:

TP181

ZHANG Yong-an, YAN Bin-bin. Deep Learning Hybrid Forecasting Model for Stock Market[J].Computer Science, 2020, 47(11): 255-267.

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