[1] Sharma, P. (2016). Forecasting stock market volatility using realized GARCH model: International evidence. The Quarterly Review of Economics and Finance, 59, 222-230.
[2] غلامی، نیما؛ و شمس قارنه، ناصر (1403). ارائه مدلی برای پیش بینی قیمت سهام مبتنی بر CNN-LSTM بهینه شده در بورس اوراق بهادار تهران. چشم انداز مدیریت مالی، 14(45)، 123–147، doi:10.48308/jfmp.2024.104892.
[3] Wang, X. (2024, September). CNN-BiLSTM-Attention Algorithm-Based Stock Prices Prediction During COVID-19. In 2024 International Conference on Artificial Intelligence and Communication (ICAIC 2024) (pp. 441-452). Atlantis Press.
[4] Rafibakhsh, R.; Khorasani, A. M.; Rezazadeh, M. The Application and Effectiveness of Machine Learning and Deep Learning Methods in Analyzing and Predicting the Shanghai Stock Index. (publication details not provided).
[5] Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159–175.
[6] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT Press.
[7] Bengio, Y., Simard, P., & Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166.
[8] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
[9] Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681.
[10] Bahdanau, D., Cho, K., & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
[11] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ..., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
[12] Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654–669.
[13] Liu, S., & Lan, Q. (2024, December). Long-term stock correlation prediction based on CNN-BiLSTM-Attention models. In Proceedings of the 4th Asia-Pacific Artificial Intelligence and Big Data Forum (pp. 1111–1117).
[14] Zhang, J., Ye, L., & Lai, Y. (2023). Stock Price Prediction Using CNN-BiLSTM-Attention Model. Mathematics, 11(9), 1985.
[15] Zhang, Y., Zhang, T., & Hu, J. (2025). Forecasting Stock Market Volatility Using CNN-BiLSTM-Attention Model with Mixed-Frequency Data. Mathematics, 13(11), 1889.
[16] Wei, E., & Wang, Z. (March 2025). Stock price prediction using CEEMD-CNN-BiLSTM-AM: A hybrid deep learning approach. In Proceedings of the 2025 5th International Conference on Applied Mathematics, Modelling and Intelligent Computing, pp. 287–295.
[17] Syed, F., & Xuan, J. (May 2025). An Attention-Augmented BiLSTM-CNN Model for Stock Price Prediction. In 2025 IEEE Conference on Artificial Intelligence (CAI), pp. 139–144, IEEE.
[18] Bhanujyothi, H. C., & Jacob, I. J. (2025). A Hybrid CNN-LSTM Attention-Based Deep Learning Model for Stock Price Prediction Using Technical Indicators. Engineering, Technology & Applied Science Research, 15(5), 28012–28017.
[19] Fatiha, L., Sarkar, S., Bashir, G. M. M., & Sarker, M. (February 2025). Stock Trend Prediction Using a Multivariate Hybrid Attention-Based BiLSTM with 1D Convolution Model. In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6. IEEE.