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Deep Learning Models for Bitcoin Prediction Using Hybrid Approaches with Gradient-Specific Optimization

Author

Listed:
  • Amina Ladhari

    (Economics, Management and Quantitative Finance Research Laboratory (LaREMFiQ), Institute of High Commercial Studies of Sousse, Economics and Quantitative Methods Department, University of Sousse, Sousse 4054, Tunisia)

  • Heni Boubaker

    (Economics, Management and Quantitative Finance Research Laboratory (LaREMFiQ), Institute of High Commercial Studies of Sousse, Economics and Quantitative Methods Department, University of Sousse, Sousse 4054, Tunisia
    IPAG Business School, 75006 Paris, France)

Abstract

Since cryptocurrencies are among the most extensively traded financial instruments globally, predicting their price has become a crucial topic for investors. Our dataset, which includes fluctuations in Bitcoin’s hourly prices from 15 May 2018 to 19 January 2024, was gathered from Crypto Data Download. It is made up of over 50,000 hourly data points that provide a detailed view of the price behavior of Bitcoin over a five-year period. In this study, we used potent algorithms, including gradient descent, attention mechanisms, long short-term memory (LSTM), and artificial neural networks (ANNs). Furthermore, to estimate the price of Bitcoin, we first merged two deep learning algorithms, LSTM and attention mechanisms, and then combined LSTM-Attention with gradient-specific optimization to increase our model’s performance. Then we integrated ANN-LSTM and included gradient-specific optimization for the same reason. Our results show that the hybrid model with gradient-specific optimization can be used to anticipate Bitcoin values with better accuracy. Indeed, the hybrid model combines the best features of both approaches, and gradient-specific optimization improves predictive performance through frequent analysis of pricing data changes.

Suggested Citation

  • Amina Ladhari & Heni Boubaker, 2024. "Deep Learning Models for Bitcoin Prediction Using Hybrid Approaches with Gradient-Specific Optimization," Forecasting, MDPI, vol. 6(2), pages 1-17, April.
    • Handle: RePEc:gam:jforec:v:6:y:2024:i:2:p:16-295:d:1380988
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    References listed on IDEAS

    as
    1. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CIRJE F-Series CIRJE-F-1078, CIRJE, Faculty of Economics, University of Tokyo.
    2. Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    3. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-430, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "Bitcoin technical trading with artificial neural network," CARF F-Series CARF-F-441, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    5. Nakano, Masafumi & Takahashi, Akihiko & Takahashi, Soichiro, 2018. "Bitcoin technical trading with artificial neural network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 587-609.
    Full references (including those not matched with items on IDEAS)

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