Cryptocurrency Value Prediction with Boosting Models

Abstract

Ensemble learning is a methodology that entails integrating a number of inefficient entities to achieve significantly improved performance. Boosting is a significant category of ensemble learning that involves the consecutive aggregate input of weak learners. The benefits of boosting approaches in processing tabular data with a significant quantity of information and resistance to overfitting can be very useful in estimating the market value of digital currency or cryptocurrency. The goal of this work is to examine and comprehend the capabilities of major boosting techniques such as XGBoost, AdaBoost, and CatBoost in cryptocurrency forecasting. The work examines the long-term forecasts of two major cryptocurrencies, Bitcoin and Ripple, for this purpose. The results indicate that AdaBoost and XGBoost have comparable predicting efficiency, followed by CatBoost. This implies that AdaBoost’s simpler boosting strategy is effective at achieving outcomes that are comparable to those of more recent boosting algorithms like XGBoost and CatBoost. The study has emphasized the similarities in achieving the best cryptocurrency prediction outcomes from each model. According to the research, a more straightforward boosting tactic is just as effective as or even more effective than the other most recent boosting strategies.