Forecasting cryptocurrency prices using Recurrent Neural Network and Long Short-term Memory

Abstract

The rapid development of cryptocurrencies over the past decade is one of the most controversial and ambiguous innovations in the modern global economy. Numerous and unpredictable fluctuations in cryptocurrencies rates, as well as the lack of intelligent and proper management of transactions of this type of currency in most developing countries and users of this type of currency, has led to increased risk and distrust of these roses in investors. Capitalists and investors prefer to invest in programs which have the least risk, the most profit and the least time to achieve the main profit. Therefore, the issue of developing appropriate methods and models for predicting the price of cryptographic products is essential both for the scientific community and for financial analysts, investors and traders. In this research, a new deep learning model is used to predict the price of cryptocurrencies. The proposed model uses a Recurrent Neural Networks (RNN) algorithm based on Long Short-Term Memory (LSTM) method to predict the price. In the presented results of the simulation of the proposed method, factors such as the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), R-Squared (R2) were compared with other similar methods. Finally, the superiority of the proposed method over other methods was proven.