[1] R. Achkar, F. Elias-Sleiman, H. Ezzidine and N. Haidar, Comparison of BPA-MLP and LSTM-RNN for stocks prediction, in 6th International symposium on computational and business intelligence (ISCBI), (2018) 48–51.
[2] G. Ahmadi and Z. Akbari, Investigating air pollutants in Lorestan province and predicting their concentration using multi-layer neural network with stable online training (case studies: Khorramabad and Poldakhter), Nivar, 48 no. 126-127 (2024) 109–126.
[3] G. Ahmadi and M. Dehghandar, Chaotic time series prediction using rough-neural networks, Math. Interdisc. Res., 8 no. 2 (2023) 71–92.
[4] C. C. Aggarwal, Neural networks and deep learning, Springer, 2018.
[5] C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning, Springer, 2006.
[6] M. Doaei, S. A. Mirzaei and M. Rafigh, Hybrid multilayer perceptron neural network with grey wolf optimization for predicting stock market index, Advances in mathematical finance and applications, 6 no. 4 (2021) 883–894.
[7] I. Goodfellow, Y. Bengio and A. Courville, Deep learning, MIT Press, 2016.
[8] S. Haykin, Neural networks and learning machines (3rd ed.), Pearson, 2009.
[9] A. Kurani, P. Doshi, A. Vakharia, et al. A Comprehensive comparative study of artificial neural network
(ANN) and support vector machines (SVM) on stock forecasting, Ann. Data. Sci., 10 (2023) 183–208.
[10] Y. C. Liang, H. P. Lee, S. P. Lim, W. Z. Lin, K. H. Lee and C. G. Wu, Proper orthogonal decomposition and its applications-part i: Theory, Journal of Sound and Vibration, 252 no. 3 (2002) 527–544.
[11] R. Karimkhani, Y. E. Tabriz and G. Ahmadi, A new hybrid method of dynamic mode decomposition and long short-term memory for financial market forecasting, Journal of Mathematics and Modeling in Finance, 3 no. 2 (2023) 1–16.
[12] B. O. Koopman, Hamiltonian systems and transformation in Hilbert space, Proceedings of the National Academy of Sciences, 17 no. 5 (1931) 315–318.
[13] J. N. Kutz, S. L. Brunton, B. W. Brunton and J. L. Proctor, Dynamic mode decomposition: data-driven modeling of complex systems, SIAM, 2016.
[14] M. Lu and X. Xu, TRNN: An efficient time-series recurrent neural network for stock price prediction, Information Sciences, 657 (2024) 119951.
[15] M. L. Minsky and S. A. Papert, Perceptrons: an introduction to computational geometry, MIT Press, Cambridge, 1968.
[16] J. Mann and J. N. Kutz, Dynamic mode decomposition for financial trading strategies, Quantitative Finance, 16 no. 11 (2016) 1643–1655.
[17] T. M. Mitchell, Machine learning (McGraw-Hill international editions computer science series), 1997.
[18] M. P. Naeini, H. Taremian and H. B. Hashemi, Stock market value prediction using neural networks, in International conference on computer information systems and industrial management applications (CISIM), (2010) 132–136.
[19] A. T. Oyewole, O. B. Adeoye, W. Afua Addy, C. C. Okoye, O. C. Ofodile and C. E. Ugochukwu, Predicting stock market movements using neural networks: A review and application study, Computer Science and IT Research Journal, 5 no. 3 (2024) 651–670.
[20] T. Peters, Data-driven science and engineering: machine learning, dynamical systems, and control, Cambridge, Cambridge University Press, 2019.
[21] F. Rosenblatt, The perceptron, a perceiving and recognizing automaton Project Para, Cornell Aeronautical Laboratory, 1957-58.
[22] P. J. Schmid, Dynamic mode decomposition of numerical and experimental data, Journal of fluid mechanics, 656 (2010) 5–28.
[23] Y. G. Song, Y. L. Zhou and R. J. Han, Neural networks for stock price prediction, arXiv preprint arXiv:1805.11317, (2018) pp. 13.
[24] G. W. Stewart, On the early history of the singular value decomposition, SIAM Review, 35 no. 4 (1993) 551–566.
[25] R. S. Sutton and A. G. Barto, Reinforcement learning: an introduction (2nd ed.), MIT Press, 2018.
[26] J. H. Tu, Dynamic mode decomposition: Theory and applications, Princeton University, 2013.
[27] D. Wu, X. Ma and D. L. Olson, Financial distress prediction using integrated Z-score and multilayer perceptron neural networks, Decision Support Systems, 159 (2022) 113814.
[28] P. Yu and X. Yan, Stock price prediction based on deep neural networks, Neural Computing and Applications, 32 no. 6 (2020) 1609–1628.
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