TY - GEN
T1 - Stacked Bidirectional Long Short-Term Memory for Stock Market Analysis
AU - Lim, Jing Yee
AU - Lim, Kian Ming
AU - Lee, Chin Poo
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/13
Y1 - 2021/9/13
N2 - Stock market prediction is a difficult task as it is extremely complex and volatile. Researchers are exploring methods to obtain good performance in stock market prediction. In this paper, we propose a Stacked Bidirectional Long Short-Term Memory (SBLSTM) network for stock market prediction. The proposed SBLSTM stacks three bidirectional LSTM networks to form a deep neural network model that can gain better prediction performance in the stock price forecasting. Unlike LSTM-based methods, the proposed SBLSTM uses bidirectional LSTM layers to obtain the temporal information in both forward and backward directions. In this way, the long-term dependencies from the past and future stock market values are encapsulated. The performance of the proposed SBLSTM is evaluated on six datasets collected from Yahoo Finance. Additionally, the proposed SBLSTM is compared with the state-of-the-art methods using root mean square error. The empirical studies on six datasets demonstrates that the proposed SBLSTM outperforms the state-of-the-art methods.
AB - Stock market prediction is a difficult task as it is extremely complex and volatile. Researchers are exploring methods to obtain good performance in stock market prediction. In this paper, we propose a Stacked Bidirectional Long Short-Term Memory (SBLSTM) network for stock market prediction. The proposed SBLSTM stacks three bidirectional LSTM networks to form a deep neural network model that can gain better prediction performance in the stock price forecasting. Unlike LSTM-based methods, the proposed SBLSTM uses bidirectional LSTM layers to obtain the temporal information in both forward and backward directions. In this way, the long-term dependencies from the past and future stock market values are encapsulated. The performance of the proposed SBLSTM is evaluated on six datasets collected from Yahoo Finance. Additionally, the proposed SBLSTM is compared with the state-of-the-art methods using root mean square error. The empirical studies on six datasets demonstrates that the proposed SBLSTM outperforms the state-of-the-art methods.
KW - Long Short-Term Memory
KW - Stacked Bidirectional Long Short-Term Memory
KW - Stock market prediction
UR - http://www.scopus.com/inward/record.url?scp=85119091085&partnerID=8YFLogxK
U2 - 10.1109/IICAIET51634.2021.9573812
DO - 10.1109/IICAIET51634.2021.9573812
M3 - Conference contribution
AN - SCOPUS:85119091085
T3 - 3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021
BT - 3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Conference on Artificial Intelligence in Engineering and Technology, IICAIET 2021
Y2 - 13 September 2021 through 15 September 2021
ER -