The rise of cryptocurrencies as a novel asset class is a result of advancements in fintech, offering substantial research opportunities. Forecasting cryptocurrency prices is a complex task due to thei..
The rise of cryptocurrencies as a novel asset class is a result of advancements in fintech, offering substantial research opportunities. Forecasting cryptocurrency prices is a complex task due to their inherent volatility and dynamic nature. In this study, we evaluate the performance of three recurrent neural network (RNN) algorithms—gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional LSTM (bi-LSTM)—in predicting the prices of Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). The models' accuracy is assessed using the mean absolute percentage error (MAPE) metric. Our findings reveal that the GRU model surpasses the LSTM and bi-LSTM models in predicting the prices of all three cryptocurrencies, establishing it as the most effective algorithm. The GRU model achieves MAPE values of 0.2454% for BTC, 0.8267% for ETH, and 0.2116% for LTC. Conversely, the bi-LSTM model demonstrates the lowest prediction accuracy, with MAPE values of 5.990% for BTC, 6.85% for ETH, and 2.332% for LTC. In summary, the prediction models proposed in this study deliver accurate results that closely align with the actual prices of cryptocurrencies.
