Open Access
Issue
SHS Web Conf.
Volume 196, 2024
2024 International Conference on Economic Development and Management Applications (EDMA2024)
Article Number 02008
Number of page(s) 6
Section Finance and Stock Market
DOI https://doi.org/10.1051/shsconf/202419602008
Published online 02 September 2024
  1. Yun, K. K., Yoon, S. W., & Won, D. (2021). Prediction of stock price direction using a hybrid GA-XGBoost algorithm with a three-stage feature engineering process. Expert Systems with Applications, 186, 115716. doi:10.1016/j.eswa.2021.115716 [CrossRef] [Google Scholar]
  2. Chandar, S. K., Sumathi, M., & Sivanandam, S. N. (2016). Prediction of stock market price using hybrid of wavelet transform and artificial neural network. Indian journal of Science and Technology, 9(8), 1-5. [CrossRef] [Google Scholar]
  3. Chen, C., Hu, J., Meng, Q., & Zhang, Y. (2011, June). Short-time traffic flow prediction with ARIMA-GARCH model. In 2011 IEEE Intelligent Vehicles Symposium (IV) (pp. 607-612). IEEE. [CrossRef] [Google Scholar]
  4. Sepp Hochreiter, Jürgen Schmidhuber; Long Short-Term Memory. Neural Comput 1997; 9 (8): 1735–1780. doi: https://doi.org/10.1162/neco.1997.9.8.1735 [CrossRef] [PubMed] [Google Scholar]
  5. Zolfaghari, M., & Gholami, S. (2021). A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction. Expert Systems with Applications, 182, 115149. [CrossRef] [Google Scholar]
  6. Branco, N. W., Cavalca, M. S. M., Stefenon, S. F., & Leithardt, V. R. Q. (2022). Wavelet LSTM for fault forecasting in electrical power grids. Sensors, 22(21), 8323. [CrossRef] [Google Scholar]
  7. Babu, C. N., & Reddy, B. E. (2014, October). Selected Indian stock predictions using a hybrid ARIMA-GARCH model. In 2014 International conference on advances in electronics computers and communications (pp. 1-6). IEEE. [Google Scholar]
  8. Babu, C. N., & Reddy, B. E. (2015). Prediction of selected Indian stock using a partitioning–interpolation based ARIMA–GARCH model. Applied Computing and Informatics, 11(2), 130-143. [CrossRef] [Google Scholar]
  9. Liu, S., Liao, G., & Ding, Y. (2018, May). Stock transaction prediction modeling and analysis based on LSTM. In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 2787-2790). IEEE. [CrossRef] [Google Scholar]
  10. Bhandari, H. N., Rimal, B., Pokhrel, N. R., Rimal, R., Dahal, K. R., & Khatri, R. K. (2022). Predicting stock market index using LSTM. Machine Learning with Applications, 9, 100320. [CrossRef] [Google Scholar]
  11. Nelson, D. M., Pereira, A. C., & De Oliveira, R. A. (2017, May). Stock market’s price movement prediction with LSTM neural networks. In 2017 International joint conference on neural networks (IJCNN) (pp. 1419-1426). Ieee. [CrossRef] [Google Scholar]
  12. Al Wadia, M. T. I. S., & Ismail, M. T. (2011). Selecting wavelet transforms model in forecasting financial time series data based on ARIMA model. Applied Mathematical Sciences, 5(7), 315-326. [Google Scholar]
  13. Gallegati, M. (2012). A wavelet-based approach to test for financial market contagion. Computational Statistics & Data Analysis, 56(11), 3491-3497. [CrossRef] [Google Scholar]
  14. Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014, March). Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th international conference on computer modelling and simulation (pp. 106-112). IEEE. [CrossRef] [Google Scholar]
  15. Sunny, M. A. I., Maswood, M. M. S., & Alharbi, A. G. (2020, October). Deep learning-based stock price prediction using LSTM and bi-directional LSTM model. In 2020 2nd novel intelligent and leading emerging sciences conference (NILES) (pp. 87-92). IEEE. [CrossRef] [Google Scholar]
  16. Tang, Q., Shi, R., Fan, T., Ma, Y., & Huang, J. (2021). Prediction of financial time series based on LSTM using wavelet transform and singular spectrum analysis. Mathematical Problems in Engineering, 2021(1), 9942410. [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.