Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (5): 239-244.DOI: 10.3778/j.issn.1002-8331.2009-0186

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Prediction of Financial Time Series by EMD-LSTM Model

YAO Honggang, MU Nianguo   

  1. School of Management, University of Shanghai for Science and Technology, Shanghai 200093, China
  • Online:2021-03-01 Published:2021-03-02



  1. 上海理工大学 管理学院,上海 200093


Aiming at the high noise and nonlinear characteristics of financial time series, this paper proposes a financial time series prediction model based on Empirical Mode Decomposition(EMD) and Long Short-Term Memory(LSTM) network. In order to avoid using the test set information in the model training process after only one empirical mode decomposition of the overall sequence, the time series data is subjected to multi-step empirical mode decomposition through a certain size time window. Next, the denoising and reconstruction of the decomposed sequence is performed, and then the reconstructed sequence is used as the input of the LSTM network to obtain the final prediction result.Using SSE composite index data,comparing it with the standard LSTM model and common prediction methods combined with EMD, the results show that the proposed EMD-LSTM model has better prediction effects.

Key words: financial time series, empirical mode decomposition, long short-term memory network, time window



关键词: 金融时间序列, 经验模态分解, 长短期记忆网络, 时间窗口