Predictive Analysis of Cryptocurrency Price Using Deep Learning

  • Authors

    • Yecheng Yao
    • Jungho Yi
    • Shengjun Zhai
    • Yuwen Lin
    • Taekseung Kim
    • Guihongxuan Zhang
    • Leonard Yoonjae Lee
    2018-08-15
    https://doi.org/10.14419/ijet.v7i3.27.17889
  • .

  • The decentralization of cryptocurrencies has greatly reduced the level of central control over them, impacting international relations and trade. Further, wide fluctuations in cryptocurrency price indicate an urgent need for an accurate way to forecast this price. This paper proposes a novel method to predict cryptocurrency price by considering various factors such as market cap, volume, circulating supply, and maximum supply based on deep learning techniques such as the recurrent neural network (RNN) and the long short-term memory (LSTM),which are effective learning models for training data, with the LSTM being better at recognizing longer-term associations. The proposed approach is implemented in Python and validated for benchmark datasets. The results verify the applicability of the proposed approach for the accurate prediction of cryptocurrency price.

  • References

    1. [1] Kaastra I & Boyd M, “Designing a neural network for forecasting financial and economic time seriesâ€, Neurocomputing, Vol.10, No.3, (1996), pp.215-236.

      [2] White H, “Economic prediction using neural networks: The case of IBM daily stock returnsâ€, IEEE International Conference on Neural Networks, (1988), pp.451-458.

      [3] Briere M, Oosterlinck K & Szafarz A, “Virtual currency, tangible return: Portfolio diversification with bitcoinâ€, Journal of Asset Management, Vol.16, No.6), (2015), pp.365-373.

      [4] Chatfield C & Yar M, “Holt-Winters forecasting: some practical issuesâ€, The Statistician, (1988), 129-140..

      [5] Karpathy A, “The unreasonable effectiveness of recurrent neural networksâ€, Andrej Karpathy blog, (2015).

      [6] Elman JL, “Finding structure in timeâ€, Cognitive science, Vol.14, No.2, (1990), pp.179-211.

      [7] Gers FA, Eck D & Schmidhuber J, “Applying lstm to time series predictable through time-window approachesâ€, Neural Nets WIRN Vietri-01, (2001), pp.669-676.

      [8] Bengio Y, Simard P & Frasconi P, “Learning long-term dependencies with gradient descent is difficultâ€, IEEE transactions on neural networks, Vol.5, No.2, (1994), pp.157-166.

      [9] Bengio Y, “Learning deep architectures for AIâ€, Foundations and trends® in Machine Learning, Vol.2, No.1, (2009), pp.1-127.

      [10] Hochreiter S & Schmidhuber J, “LSTM can solve hard long time lag problemsâ€, Advances in neural information processing systems, (1997), pp. 473-479.

      [11] Tschorsch F & Scheuermann B, “Bitcoin and beyond: A technical survey on decentralized digital currenciesâ€, IEEE Communications Surveys & Tutorials, Vol.18, No.3, (2016), pp.2084-2123.

      [12] Mukhopadhyay U, Skjellum A, Hambolu O, Oakley J, Yu L & Brooks R, “A brief survey of cryptocurrency systemsâ€, IEEE 14th Annual Conference on Privacy, Security and Trust (PST), (2016), pp.745-752.

      [13] Phillips RC & Gorse D, “Predicting cryptocurrency price bubbles using social media data and epidemic modelingâ€, IEEE Symposium Series on Computational Intelligence (SSCI), (2017), pp.1-7.

      [14] Deng Y, Bao F, Kong Y, Ren Z & Dai Q, “Deep direct reinforcement learning for financial signal representation and tradingâ€, IEEE transactions on neural networks and learning systems, Vol.28, No.3, (2017), pp.653-664.

      [15] Zhao Y, Li J & Yu L, “A deep learning ensemble approach for crude oil price forecastingâ€, Energy Economics, Vol.66, (2017), pp.9-16.

      [16] Al Shehhi A, Oudah M & Aung Z, “Investigating factors behind choosing a cryptocurrencyâ€, IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), (2014), pp.1443-1447.

      [17] Chen J & Wang D, “Long short-term memory for speaker generalization in supervised speech separationâ€, The Journal of the Acoustical Society of America, Vol.141, No.6, (2017), pp.4705-4714.

      [18] Sun L, Jia K, Chen K, Yeung DY, Shi BE & Savarese S, “Lattice long short-term memory for human action recognitionâ€, IEEE International Conference on Computer Vision, (2017).

      [19] www.kaggle.com [https://www.kaggle.com/mczielinski/bitcoin-historical-data/data]

      [20] https://coinmarketcap.com/currencies/litecoin/

      [21] Hyndman RJ & Koehler AB, “Another look at measures of forecast accuracyâ€, International journal of forecasting, Vol.22, No.4, (2006), pp.679-688.

      [22] Willmott CJ & Matsuura K, “Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performanceâ€, Climate research, Vol.30, No.1, (2005), pp.79-82.

      [23] G, Abikhanova, A Ahmetbekova, E Bayat, A Donbaeva, G Burkitbay (2018). International motifs and plots in the Kazakh epics in China (on the materials of the Kazakh epics in China), Opción, Año 33, No. 85. 20-43.

      [24] A Mukanbetkaliyev, S Amandykova, Y Zhambayev, Z Duskaziyeva, A Alimbetova (2018). The aspects of legal regulation on staffing of procuratorial authorities of the Russian Federation and the Republic of Kazakhstan Opción, Año 33. 187-216.

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  • How to Cite

    Yao, Y., Yi, J., Zhai, S., Lin, Y., Kim, T., Zhang, G., & Yoonjae Lee, L. (2018). Predictive Analysis of Cryptocurrency Price Using Deep Learning. International Journal of Engineering & Technology, 7(3.27), 258-264. https://doi.org/10.14419/ijet.v7i3.27.17889