Two-Dimensional Attention-Based LSTM Model for Stock Index Prediction


Yeonguk Yu, Yoon-Joong Kim, Journal of Information Processing Systems
Vol. 15, No. 5, pp. 1231-1242, Oct. 2019
10.3745/JIPS.02.0121
Keywords: Attention Mechanism, LSTM, Stock Index Prediction, Two-Dimensional Attention
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Abstract

This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stock index prediction, incorporating input attention and temporal attention mechanisms for weighting of important stocks and important time steps, respectively. The proposed model is designed to overcome the long-term dependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2DALSTM model is validated in a comparative experiment involving the two attention-based models multi-input LSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock data being used for training and evaluation. The model achieves superior performance compared to MI-LSTM and DARNN for stock index prediction on a KOSPI100 dataset.


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Cite this article
[APA Style]
Yeonguk Yu and Yoon-Joong Kim (2019). Two-Dimensional Attention-Based LSTM Model for Stock Index Prediction. Journal of Information Processing Systems, 15(5), 1231-1242. DOI: 10.3745/JIPS.02.0121.

[IEEE Style]
Y. Yu and Y. Kim, "Two-Dimensional Attention-Based LSTM Model for Stock Index Prediction," Journal of Information Processing Systems, vol. 15, no. 5, pp. 1231-1242, 2019. DOI: 10.3745/JIPS.02.0121.

[ACM Style]
Yeonguk Yu and Yoon-Joong Kim. 2019. Two-Dimensional Attention-Based LSTM Model for Stock Index Prediction. Journal of Information Processing Systems, 15, 5, (2019), 1231-1242. DOI: 10.3745/JIPS.02.0121.