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Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit

Author

Listed:
  • Chuen Yik Kang

    (Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia)

  • Chin Poo Lee

    (Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia)

  • Kian Ming Lim

    (Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia)

Abstract

Virtual currencies have been declared as one of the financial assets that are widely recognized as exchange currencies. The cryptocurrency trades caught the attention of investors as cryptocurrencies can be considered as highly profitable investments. To optimize the profit of the cryptocurrency investments, accurate price prediction is essential. In view of the fact that the price prediction is a time series task, a hybrid deep learning model is proposed to predict the future price of the cryptocurrency. The hybrid model integrates a 1-dimensional convolutional neural network and stacked gated recurrent unit (1DCNN-GRU). Given the cryptocurrency price data over the time, the 1-dimensional convolutional neural network encodes the data into a high-level discriminative representation. Subsequently, the stacked gated recurrent unit captures the long-range dependencies of the representation. The proposed hybrid model was evaluated on three different cryptocurrency datasets, namely Bitcoin, Ethereum, and Ripple. Experimental results demonstrated that the proposed 1DCNN-GRU model outperformed the existing methods with the lowest RMSE values of 43.933 on the Bitcoin dataset, 3.511 on the Ethereum dataset, and 0.00128 on the Ripple dataset.

Suggested Citation

  • Chuen Yik Kang & Chin Poo Lee & Kian Ming Lim, 2022. "Cryptocurrency Price Prediction with Convolutional Neural Network and Stacked Gated Recurrent Unit," Data, MDPI, vol. 7(11), pages 1-13, October.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:11:p:149-:d:958651
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    References listed on IDEAS

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    1. Srivinay & B. C. Manujakshi & Mohan Govindsa Kabadi & Nagaraj Naik, 2022. "A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network," Data, MDPI, vol. 7(5), pages 1-11, April.
    2. Salvatore Carta & Andrea Medda & Alessio Pili & Diego Reforgiato Recupero & Roberto Saia, 2018. "Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data," Future Internet, MDPI, vol. 11(1), pages 1-19, December.
    3. Aniruddha Dutta & Saket Kumar & Meheli Basu, 2020. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," JRFM, MDPI, vol. 13(2), pages 1-16, February.
    4. Mohammad Rafiqul Islam & Nguyet Nguyen, 2020. "Comparison of Financial Models for Stock Price Prediction," JRFM, MDPI, vol. 13(8), pages 1-19, August.
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    Cited by:

    1. Attilio Sbrana & Paulo André Lima de Castro, 2024. "N-BEATS Perceiver: A Novel Approach for Robust Cryptocurrency Portfolio Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1047-1081, August.
    2. Arash Peik & Mohammad Ali Zare Chahooki & Amin Milani Fard & Mehdi Agha Sarram, 2025. "Adaptive Temporal Fusion Transformers for Cryptocurrency Price Prediction," Papers 2509.10542, arXiv.org.
    3. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
    4. Godfrey Joseph Saqware & Ismail B, 2024. "Hybrid Deep Learning Model Integrating Attention Mechanism for the Accurate Prediction and Forecasting of the Cryptocurrency Market," SN Operations Research Forum, Springer, vol. 5(1), pages 1-19, March.
    5. Esteban Vanegas & Andrés Mora-Valencia, 2025. "Correction: Skew Index: a machine learning forecasting approach," Risk Management, Palgrave Macmillan, vol. 27(3), pages 1-1, September.

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