Evaluation of different types of LSTM networks for stock prediction

• Gabriel Souto CEFET/RJ
• Bruna Capistrano CEFET/RJ
• Matheus Matias CEFET/RJ
• Jorge Soares CEFET/RJ
• Eduardo Ogasawara CEFET/RJ
• Lucas Giusti CEFET/RJ

DOI: https://doi.org/10.5753/eri-rj.2021.18776

Abstract

Deep Neural Networks are valuable models in the task of learning tasks. In this work, we propose using the multilayer method known as Long Short-Term Memory. We apply three different models (LSTM Vanilla, Stacked and Convolutional) for the same stock series. This choice was made due to the gap in the literature when comparing which LSTM models to use in time series prediction. The results found proved to be an alternative to what we intend to show, in the sense of comparative work with the best LSTM models.

Keywords: Deep Learning, LSTM Models, Stock Prediction

References

B.M. Barber, Y.-T. Lee, Y.-J. Liu, and T. Odean. The cross-section of speculator skill: Evidence from day trading. Journal of Financial Markets, 18(1):1–24, 2014.

O. Bustos and A. Pomares-Quimbaya. Stock market movement forecast: A Systematic review. Expert Systems with Applications, 156, 2020.

T. Fischer and C. Krauss. Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2):654–669, 2018.

S. Hochreiter and J. Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735–1780, 1997.

Rob J. Hyndman and George Athanasopoulos. Forecasting: principles and practice. OTexts, May 2018. ISBN 978-0-9875071-1-2.

F. Kurniawan, D.E. Herwindiati, and M.D. Lauro. Raw Paper Material Stock Forecasting with Long Short-Term Memory. In 2021 9th International Conference on Information and Communication Technology, ICoICT 2021, pages 342–347, 2021.

B. Lindemann, T. Müller, H. Vietz, N. Jazdi, and M. Weyrich. A survey on long short-term memory networks for time series prediction. In Procedia CIRP, volume 99, pages 650–655, 2021.

D.M.Q. Nelson, A.C.M. Pereira, and R.A. De Oliveira. Stock market’s price movement prediction with LSTM neural networks. In Proceedings of the International Joint Conference on Neural Networks, volume 2017-May, pages 1419–1426, 2017.

M. Obthong, N. Tantisantiwong, W. Jeamwatthanachai, and G. Wills. A survey on machine learning for stock price prediction: Algorithms and techniques. In FEMIB 2020 - Proceedings of the 2nd International Conference on Finance, Economics, Management and IT Business, pages 63–71, 2020.

E.W. Saad, D.V. Prokhorov, and D.C. Wunsch II. Comparative study of stock trend prediction using time delay, recurrent and probabilistic neural networks. IEEE Transactions on Neural Networks, 9(6):1456–1470, 1998.

S. Selvin, R. Vinayakumar, E.A. Gopalakrishnan, V.K. Menon, and K.P. Soman. Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, volume 2017-January, pages 1643–1647, 2017.

X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-C. Woo. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in Neural Information Processing Systems, volume 2015-January, pages 802–810, 2015.

S. Siami-Namini, N. Tavakoli, and A. Siami Namin. A Comparison of ARIMA and LSTM in Forecasting Time Series. In Proceedings - 17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, pages 1394–1401, 2019.

Ruey S. Tsay. Analysis of Financial Time Series. Wiley, July 2010. ISBN 978-0-470-64455-3.

Y. Wu, M. Yuan, S. Dong, L. Lin, and Y. Liu. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks. Neurocomputing, 275:167–179, 2018.