Abstract: This study employs a range of machine learning models to forecast crude oil prices in Morocco, including Linear Regression, Random Forest, Support Vector Regression (SVR), XGBoost, ARIMA, Prophet and Gradient Boosting. Among these, SVR demonstrated the highest accuracy with an RMSE of 1.414. Additionally, the ARIMA and Prophet models were evaluated, yielding RMSEs of 2.46 and 1.41, respectively. An ensemble model, which combines predictions from all the individual models, achieved an RMSE of 2.144, indicating robust performance. Projections for 2024-2027 show a rising trend in crude oil prices, with the SVR model forecasting 21.91 MAD in 2027, and the ensemble model predicting 14.47 MAD. These findings underscore the effectiveness of ensemble learning and advanced machine learning techniques in producing reliable economic forecasts, offering valuable insights for stakeholders in the energy sector.

Hicham BOUSSATTA, Marouane CHIHAB and Younes CHIHAB. “A Comprehensive Study on Crude Oil Price Forecasting in Morocco Using Advanced Machine Learning and Ensemble Methods”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150743

@article{BOUSSATTA2024,
title = {A Comprehensive Study on Crude Oil Price Forecasting in Morocco Using Advanced Machine Learning and Ensemble Methods},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150743},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150743},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Hicham BOUSSATTA and Marouane CHIHAB and Younes CHIHAB}
}