Forecasting Cryptocurrency Prices Using a Gated Recurrent Unit Neural Network

Abstract

This paper investigates the potential of using a gated recurrent unit (GRU) neural network (NN) for forecasting the prices of three popular cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). A dataset spanning from October 2021 to October 2022 was collected and used to train and evaluate the performance of the proposed model. The proposed GRU model was evaluated using the root mean squared error (RMSE) and the mean absolute percentage error (MAPE) as evaluation metrics. The results of the study show that the GRU model achieved an RMSE of 366.0601 and a MAPE of 1.7268% for BTC, an RMSE of 37.6678 and a MAPE of 2.3342% for ETH, and an RMSE of 1.0902 and a MAPE of 1.7278% for LTC. The results indicate that the GRU model performed well in forecasting cryptocurrency prices and holds promise as an approach for further research in this field.