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Revolutionizing Bitcoin price forecasts: A comparative study of advanced hybrid deep learning architectures

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  • He, Xiangyi
  • Li, Yiwei
  • Li, Houjian

Abstract

This paper employs a deep learning network with a comprehensive architecture to forecast Bitcoin prices, enhancing accuracy by integrating two meta-heuristic optimization algorithms, INFO and NRBO. Empirical results demonstrate that the hybrid model significantly outperforms the LSTM in both fit and predictive accuracy across in-sample and out-of-sample data. Notably, the NRBO-CNN-BiLSTM-Attention model substantially improves accuracy in 5-day and 15-day forecasts, reducing the MAPE by over 50 % compared to the LSTM model, thereby significantly enhancing overall predictive performance. The robustness of our results is supported by the MCS tests. Furthermore, strategically modifying time steps in data analysis optimizes model performance.

Suggested Citation

  • He, Xiangyi & Li, Yiwei & Li, Houjian, 2024. "Revolutionizing Bitcoin price forecasts: A comparative study of advanced hybrid deep learning architectures," Finance Research Letters, Elsevier, vol. 69(PA).
    • Handle: RePEc:eee:finlet:v:69:y:2024:i:pa:s1544612324011656
    DOI: 10.1016/j.frl.2024.106136
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    References listed on IDEAS

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