Paper: arXiv 2510.15900

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.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: 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.

Research Flowchart

  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"]

Cited References (15)

  1. A Bayesian regularized neural network for analyzing bitcoin trends (2021)
  2. Analysis of Blockchain technology: pros, cons and SWOT (2019)
  3. Bitcoin in the economics and finance literature: a survey (2021)
  4. Comparitive automated bitcoin trading strategies (2016)
  5. Comparison of arima time series model and lstm deep learning algorithm for bitcoin price forecasting (2018)
  6. Variational mode decomposition (2013)
  7. Bayesian regression and Bitcoin (2014)
  8. Bitcoin price prediction and analysis using deep learning models (2020)
and 7 more
  1. Machine learning algorithm for cryptocurrencies price prediction (2021)
  2. Bitcoin price forecasting with neuro-fuzzy techniques (2019)
  3. Predicting the price of bitcoin using machine learning (2018)
  4. An Intelligent Predictive Maintenance Framework with Autonomous Feature Selection based on Hybrid Fuzzy Set and Rough Set Theories for Multiclass Fault Classification (2022)
  5. A comparative study of bitcoin price prediction using deep learning (2019)
  6. A GARCH-MIDAS approach to modelling stock returns (2024)
  7. AI meets economics: Can deep learning surpass machine learning and traditional statistical models in inflation time series forecasting? (2025)

0 of 15 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.