Forecasting Crude Oil Prices: Insights from Machine Learning Approaches

This study investigates the efficacy of machine learning (ML) models in forecasting crude oil prices, a critical factor influencing economic stability. Given the inherent volatility and complexity of oil markets, accurate prediction is essential for mitigating risks and informing strategic decisions. Employing an autoregressive framework, the research utilizes daily oil price data from March 2013 to February 2024 to train and evaluate a diverse set of ML algorithms, including linear regression, Random Forest, SVR, XGBoost, Gradient Boosting Machine (GBM), LSTM, and CNN. The analysis reveals that linear regression demonstrates exceptional performance, effectively capturing linear trends, while tree-based models like XGBoost and GBM also exhibit high predictive accuracy. LSTM proves adept at capturing long-term dependencies, though CNN shows superior agility in short-term forecasting. Overall, linear regression in machine learning, and LSTM from deep learning are best models in forecasting. The study underscores the importance of rigorous hyperparameter tuning and model selection based on specific forecasting objectives. However, limitations stemming from reliance on historical data and the univariate approach are acknowledged. Future research should explore incorporating external factors and hybrid model architectures to enhance forecasting robustness.