EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 196 (2024) SHS Web Conf., 196 (2024) 02001 References
Open Access
Issue
SHS Web Conf.
Volume 196, 2024
2024 International Conference on Economic Development and Management Applications (EDMA2024)
Article Number 02001
Number of page(s) 7
Section Finance and Stock Market
DOI https://doi.org/10.1051/shsconf/202419602001
Published online 26 August 2024
  1. P. Pai and C. Lin, “A hybrid ARIMA and support vector machines model in stock price prediction”, Omega, vol.33, pp. 497-505, 2005. [CrossRef] [Google Scholar]
  2. J.J. Wang, J.Z. Wang, Z.G. Zhang and S.P. Guo, “Stock index forecasting based on a hybrid model”, Omega, vol.40, pp.758-766, 2012. [CrossRef] [Google Scholar]
  3. L. Y. Wei, “A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX”, Economic Modelling, vol.33, pp. 893-899, 2013. [CrossRef] [Google Scholar]
  4. J. Patel, S. Shah, P. Thakkar, and K. Kotecha, “Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques”, Expert Systems with Applications, vol. 42, pp. 259-268, 2015. [CrossRef] [Google Scholar]
  5. R. Singh and S. Srivastava, “Stock prediction using deep learning”, Multimedia Tools and Applications, vol. 76, pp. 18569-18584, 2017. [CrossRef] [Google Scholar]
  6. G. E. P. Box and G. M. Jenkins, Time Series Analysis, Forecasting and Control. San Francisco, CA, USA: Holden-Day, 1976 [Google Scholar]
  7. G. P. Zhang, “Time series forecasting using a hybrid ARIMA and neural network model,” Neurocomputing, vol. 50, pp. 159-175, 2003. [CrossRef] [Google Scholar]
  8. Z. Niu, G. Zhong, and H. Yu, “A review on the attention mechanism of deep learning,” Neurocomputing, vol. 452, pp. 48-62, 2021. [CrossRef] [Google Scholar]
  9. A. Vaswani, N.Shazeer, N.Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS, 2017. [Google Scholar]
  10. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998. [CrossRef] [Google Scholar]
  11. C. Jin, J. Gao, Z. Shi, and H. Zhang, “Attcry: Attention-based neural network model for protein crystallization prediction,” Neurocomputing, vol. 463, pp. 265–274, 2021. [CrossRef] [Google Scholar]
  12. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997. [CrossRef] [Google Scholar]
  13. J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proceedings of the National Academy of Sciences, vol. 79, no. 8, pp. 2554-2558, 1982. [CrossRef] [PubMed] [Google Scholar]
  14. L. Mou, C. Zhou, P. Zhao, B. Nakisa, M. N. Rastgoo, R. Jain, and W. Gao, “Driver stress detection via multimodal fusion using attention-based CNN-LSTM,” Expert Systems with Applications, vol. 173, p. 114693, 2021. [CrossRef] [Google Scholar]
  15. A. Vaswani, N.Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS, 2017. [Google Scholar]
  16. I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in NeurIPS, 2014. [Google Scholar]
  17. Z. Shi, Y. Hu, G. Mo, and J. Wu, “Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction,” arXiv preprint arXiv:2204.02623, 2022. [Google Scholar]
  18. T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785-794, 2016. [Google Scholar]
  19. C. Jin, Z. Shi, K. Lin, and H. Zhang, “Predicting mirna-disease association based on neural inductive matrix completion with graph autoencoders and self-attention mechanism,” Biomolecules, vol. 12, no. 1, p. 64, 2022. [CrossRef] [Google Scholar]
  20. C. Jin, Z. Shi, W. Li, and Y. Guo, “Bidirectional lstm-crf attention-based model for chinese word segmentation,” arXiv preprint arXiv:2105.09681, 2021. [Google Scholar]
  21. S. Wiseman and A. M. Rush, “Sequence-to-sequence learning as beam-search optimization,” in EMNLP, 2016 [Google Scholar]
  22. L. Le and Y. Xie, “Recurrent embedding kernel for predicting stock daily direction,” in 2018 IEEE/ACM 5th International Conference on Big Data Computing Applications and Technologies (BDCAT), A. Sill and J. Spillner, Eds., 2018, pp. 160-166. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.