Machine Learning for Bitcoin Forecasting: Preliminary Analysis

Abstract

In the rapidly evolving landscape of cryptocurrency, accurate classification and forecasting of Bitcoin trends are crucial for investors and analysts. This article explores various machine and deep learning algorithms applied to Bitcoin classification and signal forecasting. We evaluate multiple models and we assess their effectiveness in predicting market movements. Our findings reveal that Support Vector Machines yield the best performance metrics, demonstrating superior accuracy and reliability in classifying Bitcoin data and forecasting signal trends. By leveraging these advanced techniques, this study aims to enhance understanding of market dynamics and provide actionable insights for stakeholders in the cryptocurrency domain.