Forecasting Volatility of Cryptocurrencies Using Machine Learning and Deep Learning - Mohammed Elamin, Amari; Supervisor: Lamri, Sayad (Mohamed Boudiaf University of M'sila, 2025)
This thesis investigates the application of machine learning and deep learning methods into forecasting cryptocurrencies volatility. Using Bitcoin and Ethereum datasets, the performance of the following models was analyzed: Random Forest, XGBoost, LSTM, and GRU. The results show that deep learning models, specifically LSTM, outperforms machine learning models on every metric. These findings suggest that RNN models are better equipped to handle temporal dependencies in time series data that have sequential nature.
Keywords: Machine learning; Deep learning; cryptocurrency; volatility; Bitcoin; Ethereum; Random Forest; XGBoost; LSTM; GRU