A Hybrid Attention-Based EMD-LSTM Model for Financial Time Series Prediction

Chen Lü · Yonggang Chi · Jialin Fan

53 citations0/0 files storedInternational Conference on Artificial Intelligence and Big Data (ICAIBD)

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

In order to improve the accuracy of financial time series prediction, a hybrid model is proposed in this paper which consists of the empirical mode decomposition (EMD) and the attention-based long short-term memory (LSTM-ATTE). EMD can effectively decompose financial time series into many inherent mode functions (IMFs) of multiple levels and input these IMFs into LSTM-ATTE for prediction. The attention mechanism can adaptively extract input features of the IMF and improve the accuracy of the LSTM-ATTE prediction. Finally, the predicted results are combined to obtain the final predicted results. The predictive performance of the proposed model is verified by linear regression analysis of the stock market index. In addition, by comparing the prediction results with other models, the proposed model has better performance in prediction accuracy.

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2017Stock market's price movement prediction with LSTM neural networks 2015A LSTM-based method for stock returns prediction: A case study of China stock market 2009Financial time series forecasting using independent component analysis and support vector regression 2011Forecasting stock indices with back propagation neural network 2018Improved EEMD-based crude oil price forecasting using LSTM networks 2017Forecasting stochastic neural network based on financial empirical mode decomposition 2011The use of artificial neural networks in the analysis and prediction of stock prices

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