Bitcoin Price Forecasting Based on Hybrid Variational Mode Decomposition and Long Short Term Memory Network

ArXiv ID: 2510.15900 “View on arXiv”

Authors: Emmanuel Boadi

Abstract

This study proposes a hybrid deep learning model for forecasting the price of Bitcoin, as the digital currency is known to exhibit frequent fluctuations. The models used are the Variational Mode Decomposition (VMD) and the Long Short-Term Memory (LSTM) network. First, VMD is used to decompose the original Bitcoin price series into Intrinsic Mode Functions (IMFs). Each IMF is then modeled using an LSTM network to capture temporal patterns more effectively. The individual forecasts from the IMFs are aggregated to produce the final prediction of the original Bitcoin Price Series. To determine the prediction power of the proposed hybrid model, a comparative analysis was conducted against the standard LSTM. The results confirmed that the hybrid VMD+LSTM model outperforms the standard LSTM across all the evaluation metrics, including RMSE, MAE and R2 and also provides a reliable 30-day forecast.

Keywords: Bitcoin Price Forecasting, Variational Mode Decomposition (VMD), Long Short-Term Memory (LSTM), Cryptocurrency, Time Series Decomposition

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Lab Rats
• Why: The paper employs advanced mathematical concepts such as variational mode decomposition (VMD) with detailed variational formulations and LSTM gate equations, scoring high in math complexity. However, while it presents specific backtesting results (RMSE, MAE, R2) on historical data, it lacks code, dataset access, or implementation details, and the methodology is standard for deep learning research, placing it in the Lab Rats quadrant.

  flowchart TD
    A["Research Goal<br>Bitcoin Price Forecasting"] --> B{"Data Input:<br>Historical Bitcoin Price Data"}
    B --> C["Methodology: Decomposition<br>Variational Mode Decomposition VMD"]
    C --> D["Process: Modeling<br>Long Short-Term Memory LSTM Network"]
    D --> E["Outcome: Forecasting<br>30-Day Bitcoin Price Prediction"]
    E --> F["Result: Comparison<br>VMD+LSTM vs Standard LSTM"]
    F --> G["Conclusion<br>Hybrid Model Outperforms Standard LSTM<br>RMSE, MAE, R2 Metrics"]