TY - GEN
T1 - A Hybrid Attention-Based EMD-LSTM Model for Financial Time Series Prediction
AU - Chen, Lu
AU - Chi, Yonggang
AU - Guan, Yingying
AU - Fan, Jialin
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - 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.
AB - 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.
KW - attention-based
KW - empirical mode decomposition
KW - financial time series
KW - long short-term memory
UR - https://www.scopus.com/pages/publications/85073185065
U2 - 10.1109/ICAIBD.2019.8837038
DO - 10.1109/ICAIBD.2019.8837038
M3 - 会议稿件
AN - SCOPUS:85073185065
T3 - 2019 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019
SP - 113
EP - 118
BT - 2019 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Artificial Intelligence and Big Data, ICAIBD 2019
Y2 - 25 May 2019 through 28 May 2019
ER -