Crude oil, originating from organic material deposited millions of years ago, serves as the raw material for products like gasoline, diesel, and jet fuel, highlighting its crucial role in modern industry and daily life. Predicting crude oil prices is vital for supply chain managers making operational decisions such as purchasing, production, and transportation. This research aims to predict oil prices to reduce operational costs, increase profit, and enhance competitive advantage. We employed deep learning models to capture the complex, nonlinear characteristics of crude oil price dynamics, utilizing a hyperparameter optimization framework with Bayesian optimization (Optuna) for improved convergence, reduced overfitting, and higher accuracy. In a world shaped by technological breakthroughs, geopolitical intricacies, and economic pressures, precise prediction of oil prices is challenging but essential. Advanced machine learning techniques like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were utilized. This study used datasets including WTI and BRENT to explore neural network’s ability to understand complex market linkages. Three comparative metrics—RMSE, MAD, and MAPE—ensured result reliability and application across various domains. Forecasts were conducted across three time horizons: daily, weekly, and monthly, each crucial for different stakeholders such as day traders, logistical planners, and strategic decision-makers. Daily forecasts navigate immediate market volatility, weekly forecasts inform logistical and operational adjustments, and monthly forecasts align with long-term strategic planning.
Finally, we compared traditional statistical models with the best deep learning models for both Brent and WTI crude oil using RMSE, MAD, and MAPE to assess the robustness of the deep learning approaches.