Bitcoin Price Trend Forecasting Using Hybrid Deep Learning Models

Abstract

Bitcoin price forecasting remains a complex and vital task due to the cryptocurrency market’s inherent volatility and nonlinear behavior. This paper presents a robust ensemble-based deep learning framework for predicting Bitcoin price trends. The proposed architecture integrates Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) models to effectively capture temporal dependencies and multidimensional interactions from historical market data. Utilizing a stacking ensemble strategy, the outputs of base models are combined to enhance predictive performance. Key features such as open-high-low-close (OHLC) prices, trading volumes, on-chain metrics, and sentiment indicators are extracted and pre-processed. The model is evaluated using benchmark metrics including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), demonstrating improved accuracy over standalone models. This approach underscores the efficacy of ensemble deep learning in financial time-series forecasting, offering valuable insights for traders and institutions navigating dynamic crypto markets.

Introduction

Bitcoin, launched in 2008, is a decentralized digital currency with high volatility, making accurate price prediction challenging. Traditional models like ARIMA and GARCH often fall short in capturing its nonlinear dynamics. Deep learning models, especially LSTM, GRU, and CNN, offer improved forecasting due to their ability to learn complex temporal patterns. This study proposes an ensemble deep learning framework for Bitcoin price trend prediction, leveraging multiple data sources (market, sentiment, and on-chain metrics).

Literature Review:

• Traditional Models: ARIMA, GARCH, and Bayesian networks are used but struggle with nonlinearity and adaptability.

• Feature Inputs: Effective models integrate technical indicators, macroeconomic data, and sentiment analysis.

• Feature Selection: Techniques like PCA and SHAP are used to reduce redundancy and improve interpretability.

• Deep Learning Approaches: CNN-LSTM, LSTNet, and TCN outperform traditional models but may have limitations like overfitting or computational demands.

• Gaps Identified: Limited sentiment use, static models, and lack of real-time deployment in existing studies.

Proposed Methodology:

A deep learning ensemble (mainly LSTM and GRU) is proposed for robust Bitcoin price trend forecasting. It includes:

• Data Collection: Market (Binance, Coinbase), sentiment (Google Trends, Twitter), and on-chain metrics (Glassnode).

• Preprocessing: Missing values handled with interpolation; outliers filtered using Z-score/IQR methods.

• Feature Engineering: Includes technical indicators (RSI, MACD), sentiment scores, and on-chain metrics.

• Model Optimization: Hyperparameter tuning (e.g., learning rate, dropout, layers) is applied to improve model performance.

• Evaluation Metrics: MAE, RMSE, and R² are used to assess accuracy.

Implementation:

• Software Stack: Python, TensorFlow, scikit-learn, Jupyter Notebook.

• Hardware Needs: 8GB RAM, multi-core CPU or GPU.

• Training & Testing: 80:20 split with 5-fold cross-validation; models trained using Adam optimizer and MSE loss function.

• Code Example: A sample LSTM model architecture and training loop is provided.

Results:

The ensemble model (particularly GRU) performed well on data from 2014–2024:

• MAE and RMSE: Low errors showed accurate predictions.

• R² Score: High score demonstrated strong predictive power.

• Conclusion: The model effectively forecasts Bitcoin price trends and outperforms traditional statistical methods.

Conclusion

This study demonstrates the successful implementation of a deep learning-based ensemble architecture for predicting Bitcoin price trends. By leveraging historical price data, on-chain metrics, and sentiment indicators, the system captures both short-term fluctuations and long-term dependencies in price behaviour. The integration of LSTM and GRU models enables efficient handling of sequential financial data, and feature selection techniques such as SHAP and PCA contribute to improving model interpretability and reducing overfitting. Evaluation metrics (MAE, RMSE, R²) confirm the system’s reliability and practical applicability. Ultimately, this research provides a scalable framework for financial forecasting that can support informed trading decisions and enhance the strategic planning of investors and analysts operating in cryptocurrency markets.

References

[1] S. Nakamoto, “Bitcoin: A Peer-to-Peer Electronic Cash System,” 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf [2] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. [3] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” arXiv preprint arXiv:1412.6980, 2014. [4] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2016, pp. 785–794. [5] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016. [6] D. Shen, X. Zhang, and H. Wang, “Deep Learning in Trading Bitcoin: A Survey,” Journal of Financial Markets, vol. 45, p. 101286, 2020. [7] Glassnode Analytics, “On-chain Metrics and Bitcoin Price Prediction,” 2023. [Online]. Available: https://www.glassnode.com/ [8] Binance Research, “Machine Learning in Crypto Trading: Challenges and Opportunities,” 2024. [Online]. Available: https://www.binance.com/ [9] TradingView, “Bitcoin Price Chart.” [Online]. Available: https://www.tradingview.com/symbols/BTCUSD/ [10] CoinCodex, “Bitcoin Price Prediction.” [Online]. Available: https://coincodex.com/crypto/bitcoin/price-prediction/ [11] Arxiv, “Blending Ensemble for Bitcoin Trends.” [Online]. Available: https://arxiv.org/abs/2411.03035

Copyright

Copyright © 2025 Dr. K. SubbaRao, Patan Rashia , Pulugu Ramya, Boyina Leela Bharath Kumar, Pogadadanda Iswarya, Yenugu Lahari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.