A Novel Cryptocurrency Price Prediction Model Using GRU, LSTM and bi-LSTM Machine Learning Algorithms

Cryptocurrency is a new sort of asset that has emerged as a result of the advancementof financial technology and it has created a big opportunity for researches. Cryptocurrency priceforecasting is difficult due to price volatility and dynamism. Around the world, there are hundredsof cryptocurrencies that are used. This paper proposes three types of recurrent neural network(RNN) algorithms used to predict the prices of three types of cryptocurrencies, namely Bitcoin(BTC), Litecoin (LTC), and Ethereum (ETH). The models show excellent predictions depending onthe mean absolute percentage error (MAPE). Results obtained from these models show that thegated recurrent unit (GRU) performed better in prediction for all types of cryptocurrency than thelong short-term memory (LSTM) and bidirectional LSTM (bi-LSTM) models. Therefore, it can beconsidered the best algorithm. GRU presents the most accurate prediction for LTC with MAPEpercentages of 0.2454%, 0.8267%, and 0.2116% for BTC, ETH, and LTC, respectively. The bi-LSTMalgorithm presents the lowest prediction result compared with the other two algorithms as theMAPE percentages are: 5.990%, 6.85%, and 2.332% for BTC, ETH, and LTC, respectively. Overall, theprediction models in this paper represent accurate results close to the actual prices of cryptocurrencies.The importance of having these models is that they can have significant economic ramificationsby helping investors and traders to pinpoint cryptocurrency sales and purchasing. As a plan forfuture work, a recommendation is made to investigate other factors that might affect the prices ofcryptocurrency market such as social media, tweets, and trading volume.