<![CDATA[Extreme volatility in the cryptocurrency market poses substantial financial risks, necessitating precision forecasting systems. The limitations of conventional statistical models in capturing non-linear dynamics have prompted the adoption of deep learning approaches. This study evaluates the comparative performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures in predicting the closing prices of Bitcoin (BTC), Ethereum (ETH), and Solana (SOL). Experiments utilized historical data from January 2023 to April 2026, partitioned with an 80:20 train-test ratio using a 60-day sliding window sequence. Dropout-based regularization and Early Stopping were implemented to prevent overfitting. As an empirical contribution, this research univariately examines the trade-off between memory cell complexity (LSTM) and architectural efficiency (GRU) on high-volatility data. The results demonstrate that GRU consistently outperforms LSTM across all instruments, reducing the Mean Absolute Percentage Error (MAPE) to a range of 2.93%-4.89%. Regarding computational efficiency, the GRU architecture reduced training duration by 13.33% to 36.92% compared to LSTM. Practically, these findings recommend GRU as an effective and efficient algorithmic foundation for algorithmic trading systems and digital portfolio risk management.]]>
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