Document Type : Research Article
Authors
Department of Mathematics, Payame Noor university, Tehran, Iran
Abstract
Forecasting price trends in financial markets is of particular importance for traders because price trends are inherently dynamic and forecasting these trends is complicated. In this study, we present a new hybrid method based on combination of the dynamic mode decomposition method and long short-term memory method for forecasting financial markets. This new method is in this way that we first extract the dominant and coherent data using the dynamic mode decomposition method and then predict financial market trends with the help of these data and the long short-term memory method. To demonstrate the efficacy of this method, we present three practical examples: closing price of US Dollar to Iranian Rial, closing prices of zob roy Isfahan stock, and also closing prices of siman shargh stock. These examples exhibit bullish, bearish, and neutral behaviors, respectively. It seems that the proposed new method works better in predicting the financial market than the existing long-short-term memory method.
Keywords
[2] F.A. Gers, J. Schmidhuber, F. Cummins, Learning to forget: Continual prediction with
LSTM, Neural Comput. 12 (2000), PP. 2451–2471.
[3] G. Golub, W. Kahan, Calculating the singular values and pseudo-inverse of a matrix,
Journal of the Society for Industrial and Applied Mathematics, Series B: Numerical Analysis
2(2) (1965), PP. 205–224.
[4] I. Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT press, 2016.
[5] H.S. DiStefano, Predicting long-term US housing price trends using a long short-term
memory neural network, Ph.D. Thesis, University of California, Los Angeles, 2022.
[6] A. Graves, Long short-term memory, Supervised sequence labelling with recurrent neural
networks, Stud. Comput. Intell. 385 (2012), PP. 37–45.
[7] S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Comput. 9(8) (1997),
PP. 1735–1780.
[8] J.N. Juang, R.S. Pappa, An eigensystem realization algorithm for modal parameter identification and model reduction, J. Guid. Control Dyn. 8(5) (1985), PP. 620–627.
[9] B.O. Koopman, Hamiltonian systems and transformation in Hilbert space, Proc. Natl. Acad.
Sci. USA 17 (1931), PP. 315–318.
[10] J.N. Kutz, S.L. Brunton, B.W. Brunton, J.L. Proctor, Dynamic mode decomposition:
data-driven modeling of complex systems, SIAM, 2016.
[11] J. Mann, J.N. Kutz, Dynamic mode decomposition for financial trading strategies, Quant.
Finance 16 (2016), PP. 1643–1655.
[12] M. Paluszek, S. Thomas, Practical matlab deep learning, A Project-Based Approach, Apress
Berkeley, CA, 2020.
[13] P.J. Schmid, Dynamic mode decomposition of numerical and experimental data, J. Fluid
Mech. 656 (2010), PP. 5–28.
[14] P.J. Schmid, J. Sesterhenn, Dynamic mode decomposition of numerical and experimental
data, In Bull. Amer. Phys. Soc., 61st APS meeting, p. 208. San Antonio, 2008.
[15] S. Siami Namini, A. Siami Namini, Forecasting economics and financial time series: ARIMA
vs. LSTM, arXiv:1803.06386 (2018).
[16] J.H. Tu, Dynamic mode decomposition: Theory and applications, Ph.D. Thesis, Princeton
University, 2013.
[17] J.H. Tu, C.W. Rowley, D.M. Luchtenburg, S. L. Brunton, J.N. Kutz, On dynamic
mode decomposition: Theory and applications, Journal of Computational Dynamics 1 (2014),
PP. 391–421.
[18] S. Yao, L. Luo, H. Peng, High-frequency stock trend forecast using LSTM model, 13th
International Conference on Computer Science & Education (ICCSE), IEEE, (2018), PP.
1–4.
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