Analyzing the Effectiveness of Machine Learning Models for Next Day Cryptocurrency Price Prediction

Abstract

Crypto currencies represent a digital form of currency, entirely reliant on electronic transactions and lacking a physical counterpart in the form of traditional banknotes. Unlike fiat currencies, they operate in a decentralized manner, free from third-party intervention, enabling users to access services directly. Nevertheless, the volatility in cryptocurrency prices significantly impacts international relations and trade, contributing to economic inequalities on a global scale. This research concentrates on Bitcoin price prediction, a highly popular cryptocurrency widely accepted by various stakeholders, including investors, researchers, traders, and policymakers. Therefore, in this research, it is proposed to compare the performance of two machine learning models (a RNN model and a LSTM model) for bitcoin price prediction. Open and close pricing are the main requirements for implementation of the model. In addition, the research compares the accuracy values of both models for closed prices, contributing to Sustainable Cities and Communities. The results prove that LSTM model is a better choice than RNN model with a very low error of 0.196%.