Forecasting global oil demand: Application of machine learning techniques - Ahmad M. Aldabbagh; Andreas Economou; Christou Chariton (The Oxford Institute for Energy Studies, 2024)
This study introduces a novel approach to predicting global oil demand by integrating machine learning (ML) techniques to forecast consumption across seven refined oil products and seven key regions. By aggregating these forecasts, we offer a comprehensive view of global demand trends. The paper examines the efficacy of ML models in providing robust and accurate demand forecasts. It also provides a transparent and repeatable process to forecast oil demand. A comparison between the extreme gradient boosting (XGBoost) model and Neural Hierarchical Interpolation for Time Series Forecasting (N-HiTS) model was conducted to determine which is a more accurate model to forecast demand. Our comparative analysis demonstrates that N-HiTS performs better. The accuracy of global oil demand forecasts is pivotal for economic planning and policy making.
Keywords: 330; Forecast Scenarios; machine learning; Oil Demand; oil forecasting; oil market outlook; oil products