EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 196 (2024) SHS Web Conf., 196 (2024) 02004 References
Open Access
Issue
SHS Web Conf.
Volume 196, 2024
2024 International Conference on Economic Development and Management Applications (EDMA2024)
Article Number 02004
Number of page(s) 6
Section Finance and Stock Market
DOI https://doi.org/10.1051/shsconf/202419602004
Published online 26 August 2024
  1. Zhang, J., & Man, K. F. (1998, October). Time series prediction using RNN in multi-dimension embedding phase space. In SMC’98 conference proceedings. 1998 IEEE international conference on systems, man, and cybernetics (cat. no. 98CH36218) (Vol. 2, pp. 1868-1873). IEEE. [CrossRef] [Google Scholar]
  2. Khaki, S., Wang, L., & Archontoulis, S. V. (2020). A CNN-RNN framework for crop yield prediction. Frontiers in Plant Science, 10, 1750. [CrossRef] [Google Scholar]
  3. Fan, J., Li, Q., Hou, J., Feng, X., Karimian, H., & Lin, S. (2017). A spatiotemporal prediction framework for air pollution based on deep RNN. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 15-22. [CrossRef] [Google Scholar]
  4. Pawar, K., Jalem, R. S., & Tiwari, V. (2019). Stock market price prediction using LSTM RNN. In Emerging Trends in Expert Applications and Security: Proceedings of ICETEAS 2018 (pp. 493-503). Springer Singapore. [Google Scholar]
  5. Shin, D. H., Choi, K. H., & Kim, C. B. (2017). Deep learning model for prediction rate improvement of stock price using RNN and LSTM. The Journal of Korean Institute of Information Technology, 15(10), 9-16. [CrossRef] [Google Scholar]
  6. Altché, F., & de La Fortelle, A. (2017, October). An LSTM network for highway trajectory prediction. In 2017 IEEE 20th international conference on intelligent transportation systems (ITSC) (pp. 353-359). IEEE. [Google Scholar]
  7. Gers, F. A., Schmidhuber, J., & Cummins, F. (2000). Learning to forget: Continual prediction with LSTM. Neural computation, 12(10), 2451-2471. [CrossRef] [PubMed] [Google Scholar]
  8. Zhang, X., Liang, X., Zhiyuli, A., Zhang, S., Xu, R., & Wu, B. (2019, July). At-lstm: An attention-based lstm model for financial time series prediction. In IOP Conference Series: Materials Science and Engineering (Vol. 569, No. 5, p. 052037). IOP Publishing. [Google Scholar]
  9. Xue, H., Huynh, D. Q., & Reynolds, M. (2018, March). SS-LSTM: A hierarchical LSTM model for pedestrian trajectory prediction. In 2018 IEEE winter conference on applications of computer vision (WACV) (pp. 1186-1194). IEEE. [Google Scholar]
  10. Wang, J., Li, J., Wang, X., Wang, J., & Huang, M. (2021). Air quality prediction using CT-LSTM. Neural Computing and Applications, 33, 4779-4792. [CrossRef] [Google Scholar]
  11. Lu, W., Li, J., Wang, J., & Qin, L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications, 33(10), 4741-4753. [CrossRef] [Google Scholar]
  12. Hoseinzade, E., & Haratizadeh, S. (2019). CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129, 273-285. [CrossRef] [Google Scholar]
  13. Kim, T. Y., & Cho, S. B. (2019). Predicting residential energy consumption using CNN-LSTM neural networks. Energy, 182, 72-81. [CrossRef] [Google Scholar]
  14. Lane, N., & Kahanda, I. (2021). DeepACPpred: a novel hybrid CNN-RNN architecture for predicting anti-cancer peptides. In Practical Applications of Computational Biology & Bioinformatics, 14th International Conference (PACBB 2020) 14 (pp. 60-69). Springer International Publishing. [Google Scholar]
  15. Elman, J. L. (1990). Finding structure in time. Cognitive science, 14(2), 179-211. [CrossRef] [Google Scholar]
  16. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. [CrossRef] [Google Scholar]
  17. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989). Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4), 541-551. [CrossRef] [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.