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Crypto Currency Price Forecast: Neural Network Perspectives

Author

Listed:
  • Yuriy Kleban

    (National University of Ostroh Academy)

  • Tetiana Stasiuk

    (National University of Ostroh Academy)

Abstract

TThe study examines the problem of modeling and forecasting the price dynamics of crypto currencies. We use machine learning techniques to forecast the price of crypto currencies. The FB Prophet time series model and the LSTM recurrent neural network were selected to implement the study. Using the example of data from Binance (the most popular exchange in Ukraine) for the period from 06.07.2020 to 01.04.2023, prices for Bitcoin, Ethereum, Ripple, and Dogecoin were modeled and forecasted. The recurrent neural network of long-term memory showed significantly better results in forecasting according to the RMSE, MAE, and MAPE criteria, compared to the Naive model, the traditional ARIMA model, and the FB Prophet results.

Suggested Citation

  • Yuriy Kleban & Tetiana Stasiuk, 2022. "Crypto Currency Price Forecast: Neural Network Perspectives," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 254, pages 29-42.
    • Handle: RePEc:ukb:journl:y:2022:i:254:p:29-42
    DOI: 10.26531/vnbu2022.254.03
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    File URL: https://journal.bank.gov.ua/en/article/2022/254/03
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    References listed on IDEAS

    as
    1. Cheng, Jiyang & Tiwari, Sunil & Khaled, Djebbouri & Mahendru, Mandeep & Shahzad, Umer, 2024. "Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    2. D. Couts & D. Grether & M. Nerlove, 1966. "Forecasting Non-Stationary Economic Time Series," Management Science, INFORMS, vol. 13(1), pages 1-21, September.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    forecasting; neural networks; crypto currency; time series;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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