Bitcoin is so far is been the most versatile form of the cryptocurrency we came across in recent times, and the one which is widely accepted as well. Its values are varying like anything as we can see the frequent variation in the market value. We can say that this variation is dependent on various factors which a simple linear form of an equation or the method may fail to predict. In such a condition, it is very important that we apply a more efficient way of prediction. Several methods were employed having mathematical models which didn’t give out the expected results. Deep learning methods are widely known to solve such conditions, due to which the Recurrent Neural Networks come into the picture with its ability to learn the problem with the previous literature data. It can analyze the previous value and variations in the bitcoin pricing and using it as its base of knowledge, it can make the predictions more accurate. Even more by restructuring the activation function inside the Recurrent Neural Networks, its prediction accuracy can be further improved.

Activation Function, Bitcoin, Deep Learning,Prediction, Recurrent Neural Networks

[1] S. Karasu, A. Altan, Z. Sarac, and R. Hacioglu, “Prediction of Bitcoin prices with machine learning methods using time series data,” in 2018 26th Signal Processing and Communications Applications Conference (SIU), 2018, pp. 1–4.
[2] A. K. Jain, Jianchang Mao, and K. M. Mohiuddin, “Artificial neural networks: a tutorial,” Computer (Long. Beach. Calif)., vol. 29, no. 3, pp. 31–44, Mar. 1996.
[3] P. Giudici and I. Abu-Hashish, “What determines bitcoin exchange prices? A network VAR approach,” Financ. Res. Lett., no. February, pp. 0–1, 2018.
[4] T. Panagiotidis, T. Stengos, and O. Vravosinos, “On the determinants of bitcoin returns: A LASSO approach,” Financ. Res. Lett., vol. 27, no. February, pp. 235–240, 2018.
[5] H. A. Aalborg, P. Molnár, and J. E. de Vries, “What can explain the price, volatility and trading volume of Bitcoin?,” Financ. Res. Lett., no. July, pp. 1–11, 2018.
[6] E. Demir, G. Gozgor, C. K. M. Lau, and S. A. Vigne, “Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation,” Financ. Res. Lett., vol. 26, no. January, pp. 145–149, 2018.
[7] G. S. Atsalakis, I. G. Atsalaki, F. Pasiouras, and C. Zopounidis, “Bitcoin price forecasting with neuro-fuzzy techniques,” Eur. J. Oper. Res., 2019.
[8] F. N. Zargar and D. Kumar, “Informational inefficiency of Bitcoin: A study based on high-frequency data,” Res. Int. Bus. Financ., vol. 47, no. April 2018, pp. 344–353, 2018.
[9] A. C. da Silva Filho, N. D. Maganini, and E. F. de Almeida, “Multifractal analysis of Bitcoin market,” Phys. A Stat. Mech. its Appl., vol. 512, pp. 954–967, 2018.
[10] M. El Alaoui, E. Bouri, and D. Roubaud, “Bitcoin price–volume: A multifractal cross-correlation approach,” Financ. Res. Lett., no. September, pp. 1–8, 2018.
[11] D. Stosic, D. Stosic, T. B. Ludermir, and T. Stosic, “Collective behavior of cryptocurrency price changes,” Phys. A Stat. Mech. its Appl., vol. 507, pp. 499–509, 2018.
[12] M. Nakano, A. Takahashi, and S. Takahashi, “Bitcoin technical trading with artificial neural network,” Phys. A Stat. Mech. its Appl., vol. 510, pp. 587–609, 2018.
[13] K. Cho et al., “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation,” Jun. 2014.
[14] S. Bouktif et al., “Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †,” Energies, vol. 11, no. 7, p. 1636, Jun. 2018.
[15] A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 6645–6649.
[16] S. Hochreiter and J. Urgen Schmidhuber, “Ltsm,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, 1997.
[17] Xin Yao, “Evolving artificial neural networks,” Proc. IEEE, vol. 87, no. 9, pp. 1423–1447, 1999.
[18] L. O. Chua and T. Roska, “The CNN paradigm,” IEEE Trans. Circuits Syst. I Fundam. Theory Appl., vol. 40, no. 3, pp. 147–156, Mar. 1993.
[19] C.-Y. Zhang, C. L. P. Chen, M. Gan, and L. Chen, “Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting,” IEEE Trans. Sustain. Energy, vol. 6, no. 4, pp. 1416–1425, Oct. 2015.
[20] G. Diakun, L. Fairall, and A. Klug, “© 1986 Nature Publishing Group,” Nature, vol. 324, pp. 698–699, 1986.
[21] “Top Research Papers On Recurrent Neural Networks For NLP Enthusiasts.” [Online]. Available: https://www.analyticsindiamag.com/top-research-papers-on-recurrent-neural-networks-for-nlp-enthusiasts/. [Accessed: 21-Jan-2019].
[22] Sin-Horng Chen, Shaw-Hwa Hwang, and Yih-Ru Wang, “An RNN-based prosodic information synthesizer for Mandarin text-to-speech,” IEEE Trans. Speech Audio Process., vol. 6, no. 3, pp. 226–239, May 1998.