Short-Term Bitcoin Forecasting Across Multiple Horizons: The Effectiveness of GRU Networks

Tóm tắt

This study proposed a GRU-based framework for short-term multi-horizon Bitcoin forecasting and validated its effectiveness through a systematic comparison with four state-of-the-art baseline architectures: Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), one-dimensional Convolutional Neural Networks (1D-CNN), and Transformer networks over multiple time horizons. Based on daily Bitcoin price data spanning from September 17, 2014, to June 11, 2025, the models were systematically evaluated under three forecasting horizons—3-day, 5-day, and 7-day—using various input window sizes. Model performance was quantitatively assessed through five standard metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and the Coefficient of Determination (R²). Experimental results demonstrate that the GRU model consistently outperforms the other architectures in terms of predictive accuracy, temporal stability, and computational efficiency across all tested configurations. Notably, this study provides one of the first comprehensive benchmarks of these deep learning models under consistent experimental settings, offering practical guidance for selecting forecasting architectures in highly volatile cryptocurrency markets.