牛津能源研究所（OIES）：2025全球石油需求预测报告：机器学习技术的应用（英文版）（32页）.pdf

1、 November 2024 OIES Paper Ahmad M.Aldabbagh,Visiting Research Fellow OIES Andreas Economou,Head of Oil Research OIES Chariton Christou,Research Associate OIES Forecasting Global Oil Demand:Application of Machine Learning Techniques The contents of this paper are the authors sole responsibility.They

2、do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.2 Abstract 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

3、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 extrem

4、e 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

5、is pivotal for economic planning and policy making.3 The contents of this paper are the authors sole responsibility.They do not necessarily represent the views of the Oxford Institute for Energy Studies or any of its Members.1.Introduction Accurate forecasting of oil demand is critical for strategic

6、 planning.Traditional econometric models,while useful,often struggle to capture the complex dynamics influenced by numerous economic indicators and geopolitical factors.These models typically rely on linear assumptions and may not adequately address the non-linear relationships inherent in oil marke