Comparative performance of machine learning algorithms for Cardano cryptocurrency forecasting

Abstract

Cardano is an open-source and decentralized public blockchain platform, with consensus achieved using proof of stake. It can facilitate peer-to-peer transactions with its internal cryptocurrency, ADA, with no third-party involvement. In recent years, machine learning has been proliferating and has made many theoretical breakthroughs that find its application in many fields. The study of the machine learning approach in price prediction in Bitcoin and Ethereum has gained much attention, while relatively little research focuses on ADA forecasting. The experiment objective is to investigate the prediction of ADA's short period future prices dealing with real-world data. A comparative study of the results produced by different machine learning models, data visualizations, and statistical approaches. The experiment indicates that Gradient Boosting is the best-suited algorithm that can be selected to predict future ADA prices for short-term trading strategies.