Two-Dimensional Attention-Based LSTM Model for Stock Index Prediction
1
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stockindex prediction, incorporating input attention and temporal attention mechanisms for weighting of importantstocks and important time steps, respectively. The proposed model is designed to overcome the long-termdependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2DALSTMmodel is validated in a comparative experiment involving the two attention-based models multi-inputLSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock databeing used for training and evaluation. The model achieves superior performance compared to MI-LSTM andDARNN for stock index prediction on a KOSPI100 dataset.
Abstract
This paper presents a two-dimensional attention-based long short-memory (2D-ALSTM) model for stockindex prediction, incorporating input attention and temporal attention mechanisms for weighting of importantstocks and important time steps, respectively. The proposed model is designed to overcome the long-termdependency, stock selection, and stock volatility delay problems that negatively affect existing models. The 2DALSTMmodel is validated in a comparative experiment involving the two attention-based models multi-inputLSTM (MI-LSTM) and dual-stage attention-based recurrent neural network (DARNN), with real stock databeing used for training and evaluation. The model achieves superior performance compared to MI-LSTM andDARNN for stock index prediction on a KOSPI100 dataset.
참고문헌 (10)
- J. Cao, "Stock price forecasting model based on modified convolution neural network and financial time series analysis", International Journal Communication and Systems, 32(12) (2019)
- K. Greff, "LSTM: a search space odyssey", IEEE Transactions on Neural Networks and Learning Systems, 28(10), pp.2222~2232 (2016)
- E. W. Saad, "Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks", IEEE Transactions on Neural Networks, 9(6), pp.1456~1470 (1998)
- Z. Berradi, "Integration of principal component analysis and recurrent neural network to forecast the stock price of Casablanca stock exchange", Procedia Computer Science, 148, pp.55~61 (2019)
- D. L. Minh, "Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network", IEEE Access, 6, pp.55392~55404 (2018)
- Y. Baek, "ModAugNet: a new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module", Expert Systems with Applications, 113, pp.457~480 (2018)
- "Neural machine translation by jointly learning to align and translate" (2014)[URL]
- G. Liu, "A numerical-based attention method for stock market prediction with dual information", IEEE Access, 7, pp.7357~7367 (2019)
- H. Li, "Stock price prediction using attention-based multi-Input LSTM", Proceedings of the 10th Asian Conference on Machine Learning, pp.454~469 (2018)
- Y. Qin, "A dual-stage attention-based recurrent neural network for time series prediction", Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp.2627~2633 (2017)