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Multi-scale decomposition for deep learning-based Bitcoin price forecasting

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  • Wang, Sidan
  • Huang, Zidong
  • Liao, Zhong-Wei

Abstract

Bitcoin’s high volatility poses persistent challenges for accurate forecasting, and existing models often overlook the market’s multi-scale structure. To address this issue, we propose a forecasting framework that integrates variational mode decomposition (VMD) with several deep learning models. Using hourly Bitcoin price data from January 1, 2016 to June 30, 2025, the empirical results show that VMD consistently improves predictive performance across model architectures, with the VMD-enhanced convolutional neural network (CNN) achieving the best results. Additional analysis of sliding-window lengths suggests that shorter windows provide more timely and robust forecasts during highly volatile periods. These findings underscore the effectiveness of multi-scale decomposition as a model-agnostic enhancement strategy for cryptocurrency price prediction.

Suggested Citation

  • Wang, Sidan & Huang, Zidong & Liao, Zhong-Wei, 2026. "Multi-scale decomposition for deep learning-based Bitcoin price forecasting," Finance Research Letters, Elsevier, vol. 96(C).
    • Handle: RePEc:eee:finlet:v:96:y:2026:i:c:s1544612326003065
    DOI: 10.1016/j.frl.2026.109776
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