A Hybrid Deep Ensemble Learning Framework for Cryptocurrency Price Prediction

Abstract

This study aimed to develop and evaluate a hybrid deep ensemble learning framework integrating CNN-LSTM, GRU, and Transformer architectures through stacking for accurate next-day cryptocurrency price prediction. This quantitative predictive study analyzed daily market data for five major cryptocurrencies, including Bitcoin, Ethereum, Binance Coin, Ripple, and Cardano, over the period from January 1, 2018, to December 31, 2025. The dataset comprised 14,610 cryptocurrency-day observations, with 2,922 observations for each asset. Data were chronologically divided into training, validation, and testing subsets. Historical price, trading volume, return, volatility, lagged variables, and technical indicators were used as predictors. A 30-day sliding window was employed to predict the following day’s closing price. The proposed framework combined CNN-LSTM, GRU, and Transformer models using a stacking-based meta-learner. Performance was evaluated using Mean Absolute Error, Root Mean Squared Error, Mean Absolute Percentage Error, and R². Diebold-Mariano tests were used to compare out-of-sample forecast accuracy. The proposed hybrid ensemble achieved an MAE of 0.0294, RMSE of 0.0436, MAPE of 3.12%, and R² of 0.971, outperforming CNN-LSTM, GRU, Transformer, ARIMA, Random Forest, XGBoost, and persistence benchmarks. Diebold-Mariano tests showed that the ensemble produced significantly lower forecast loss than all competing models, including Transformer, the strongest standalone model, with all p values below 0.001. Cryptocurrency-specific analyses yielded R² values above 0.960 for all five assets, while volatility-regime analysis showed that predictive accuracy declined under high-volatility conditions but remained strong, with an R² of 0.952. The findings demonstrate that combining convolutional, recurrent, and attention-based deep-learning mechanisms through stacking can significantly improve the accuracy and robustness of cryptocurrency price forecasting across heterogeneous assets and changing volatility conditions.