Comparative Time Series Forecasting of Major Cryptocurrencies Using the GRU Deep Neural Network - I Putu Bramasta Priadinata; I Gede Iwan Sudipa; Sani Inusa Milala (Yayasan Sinergi Widya Nusantara (Sidyanusa), 2025)
Cryptocurrency investments are increasingly popular due to their potential as digital assets, but high price volatility remains a major challenge in making investment decisions. This study implements the Gated Recurrent Unit (GRU) model to forecast the closing prices of five popular cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Binance Coin (BNB), and Dogecoin (DOGE), using historical datasets from Yahoo Finance covering the period from November 30, 2019, to November 29, 2024. Performance evaluation was conducted using Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R²). The results show that the GRU model achieved the best performance for BNB with a MAPE of 2.38% and an RMSE of 17.03, followed by ETH and XRP with MAPE values of 2.51% and 2.64%, respectively. BTC recorded the highest RMSE of 2280.73, reflecting its significant price volatility, while DOGE exhibited the lowest RMSE of 0.01 despite having the highest MAPE of 4.11%. Forecasts for the next six periods indicate that BTC and ETH are likely to experience gradual price increases, XRP and BNB show a flattening trend, and DOGE remains stable with low volatility. This study concludes that the GRU model is effective in forecasting cryptocurrency prices; however, it is recommended to complement the results with fundamental and technical analysis to improve accuracy and support more optimal investment decision-making.