Predicting Cryptocurrency Price Using Multiple Deep Learning Models

The development of financial technology has led to the emergence of a brand-new kind of asset known as cryptocurrency. In general, several cryptocurrencies are present all around the world, but Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH) are considered as best cryptocurrencies. A recurrent neural network (RNN) is best suited for forecasting the prices of several cryptocurrencies. The models offer reliable forecasts based on the mean absolute percentage error (MAPE). In the proposed work, we want to show how three RNN models—the gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional LSTM (bi-LSTM) models—perform while also examining the MAPE percentages. According to our experimental data, GRU outperforms the other two cryptocurrencies with a very low error rate. As a result, GRU is the model of choice for making more accurate and timely forecasts about the price of cryptocurrencies.