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Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators
Conference proceeding   Peer reviewed

Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators

Abdelatif Hafid, Maad Ebrahim, Mohamed Rahouti and Diogo Oliveira
IEEE International Performance, Computing, and Communications Conference, pp.1-6
IEEE International Performance, Computing, and Communications Conference (IPCCC) (Orlando, Florida, USA, 11/22/2024–11/24/2024)
01/27/2025
DOI: https://doi.org/10.1109/IPCCC59868.2024.10850357
Web of Science ID: WOS:001447876900050

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Abstract

Bitcoin Market forecasting Price prediction Regression analysis XGBoost Machine Learning Securities Markets
The rapid growth of the stock market has attracted many investors due to its profit potential. However, accurately predicting stock prices is challenging due to the complexity and volatility of financial markets, especially in the cryptocurrency sector. This study presents a machine learning approach to predict cryptocurrency prices using technical indicators like Exponential Moving Average (EMA) and Moving Average Convergence Divergence (MACD) with an XGBoost regressor model. Focusing on Bitcoin's closing prices, we evaluate the model's performance through simulations, demonstrating promising results that suggest its potential to assist cryptocurrency traders and investors in dynamic market conditions.

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