BTC, ETH and Dogecoin Price Prediction Based on OLS, Random Forest and XGBoost

Abstract

In recent years, the digital world is fast speeding developed from decentralised concept to blockchain, then to cryptocurrency. Especially, cryptocurrency is a popular trending in recent decades that attracts different experts from various field. Its high volatility has been attracted plenty of investors while also brings the difficulty for realizing the price forecasting. On this basis, this study uses public cryptocurrency dataset and three analytical models to predict the direction of cryptocurrency’s price. To be specific, three underlying assets covering large proportion in cryptocurrency are selected, i.e., Bitcoin, Ethereum and Dogecoin. According to the analysis, the prediction results of different models and approaches will be presented. At the end of study, it gains that the optional model with appropriate hyperparameters based on the judgement of metrics values, which offers relevant suggestions for future works. These results shed light on guiding further exploration of cryptocurrency price prediction in terms the state-of-art machine learning scenarios.