A Stacking Ensemble Deep Learning Model for Stock Price Forecasting
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5663-6_13
Reference27 articles.
1. Li, Q., Tan, J., Wang, J., et al.: A multimodal event-driven LSTM model for stock prediction using online news. IEEE Trans. Knowl. Data Eng. 33(10), 3323–3337 (2020)
2. Jing, N., Wu, Z., Wang, H.: A hybrid model integrating deep learning with investor sentiment analysis for stock price prediction. Expert Syst. Appl. 178, 115019 (2021)
3. Daiya, D., Lin, C.: Stock movement prediction and portfolio management via multimodal learning with transformer. In: ICASSP 2021–2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3305–3309. IEEE (2021)
4. Ang, G., Lim, E.P.: Guided attention multimodal multitask financial forecasting with inter-company relationships and global and local news. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 6313–6326 (2022)
5. Wang, J., Hu, Y., Jiang, T.X., et al.: Essential tensor learning for multimodal information-driven stock movement prediction. Knowl.-Based Syst. 262, 110262 (2023)
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