Forecasting Volatility of Cryptocurrencies Using Machine Learning and Deep Learning
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Date
2025-06-15
Journal Title
Journal ISSN
Volume Title
Publisher
Mohamed Boudiaf University of M'sila
Abstract
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.
Description
Keywords
Machine learning, Deep learning, cryptocurrency, volatility, Bitcoin, Ethereum, Random Forest, XGBoost, LSTM, GRU