Forecasting oil futures markets using machine learning and seasonal trend decomposition

Abstract

ABSTRACT Can machine learning improve prediction for seasonal commodity prices? We explore the effectiveness of a combined method that integrates seasonal trend decomposition using LOESS (STL) with machine learning (ML), referred to as STL-ML. We apply Extreme Gradient Boosting and Random Forest to forecast oil futures price dynamics over a sample period from 2004 to 2023. Our STL-ML results indicate no significant improvement for crude oil futures but enhanced accuracy for heating oil futures, highlighting STL’s benefit for datasets with strong seasonality. We demonstrate the potential for machine learning performance enhancement with STL, emphasising the variability of predictive model effectiveness due to data characteristics and providing insights for refining investment strategies based on seasonality and trends.