A Gated Recurrent Unit Approach to Bitcoin Price Prediction

Analysis

Why This Paper Matters

Bitcoin price prediction remains a challenging problem due to high volatility and non-linear dynamics. This paper matters because it systematically compares advanced sequence models (GRU, LSTM, RNN) on a common feature set, showing that GRU with recurrent dropout outperforms the widely-used LSTM. The inclusion of a simple trading strategy that yields financial gain bridges the gap between academic forecasting and real-world application, making the work relevant for both researchers and practitioners in cryptocurrency markets.

Technical Contributions

• GRU with recurrent dropout: The key innovation is applying recurrent dropout to the GRU architecture, which regularizes the model and prevents overfitting on noisy Bitcoin price data.
• Fixed feature engineering: The authors use a consistent set of exogenous (e.g., market indicators) and endogenous (e.g., past prices) factors, ensuring fair comparison across models.
• Comprehensive baseline comparison: The study benchmarks against RNN, LSTM, and traditional time series models, using RMSE as the primary metric.
• Trading strategy integration: The paper demonstrates that model predictions can be directly used in a simple trading rule (e.g., buy/sell signals) to generate profit, validating practical utility.

Results

The experimental results show that the GRU model with recurrent dropout achieves the lowest RMSE among all tested models. Specifically, it outperforms LSTM and RNN, which are common baselines in cryptocurrency prediction. The trading strategy based on GRU predictions yields positive returns, indicating that the model captures meaningful price trends. However, the paper does not report exact RMSE values or profit percentages in the abstract, so readers should consult the full text for precise numbers.

Significance

This paper contributes to the growing field of deep learning for financial time series by highlighting the GRU architecture's advantages over LSTM in terms of computational efficiency and generalization (via dropout). It also provides a reproducible framework for feature engineering and model evaluation. For AI practitioners, the work underscores the importance of regularization (recurrent dropout) in sequence models for volatile data. The demonstration of profitable trading strategies adds practical value, though real-world deployment would require careful consideration of transaction costs and market impact.