Abstract

Bitcoin, as a decentralized digital currency, has undergone extreme price fluctuations over the years. Predicting its future price presents a significant challenge due to its volatile nature and susceptibility to various external factors, including market sentiment, regulations, and technological developments. This research aims to build an advanced forecasting model to predict Bitcoin’s price movements accurately. We leverage historical price data and apply cutting-edge machine learning techniques, including Long Short-Term Memory (LSTM) networks and Gradient Boosting Machines (GBM). By comparing these methods with existing models, we evaluate their performance in predicting Bitcoin’s future prices, which is vital for traders, investors, and financial institutions in making informed decisions.