Cryptocurrency Price Prediction using Optimised LSTM with GRU (Gated Recurrent Unit)

Abstract

The development of financial technology has given rise to a new kind of asset called cryptocurrency, which has presented a significant potential for study. Forecasting cryptocurrency prices is challenging because of their dynamism and unpredictability. Each of the three recurrent neural network, or RNN, algorithms proposed in this paper may be used to predict the prices of three distinct cryptocurrency types: Bitcoin (BTC), Litecoin (LTC), and Ethereum (ETH). The algorithms generate accurate forecasts based on the average absolute percentage error (MAPE). For both cryptocurrency variations, the gated recurrent unit (GRU) fared better in terms of prediction than the long short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models, according to the models' results. It is therefore regarded as the best algorithm.