An attention-PCA based forecast combination approach to crude oil price

Abstract

Crude oil price forecasting has garnered considerable attention due to its pivotal role in both market dynamics and economic stability. In this study, we present an attention-based principal component analysis (attention-PCA) methodology designed to improve the performance of oil price forecasting models. The attention-PCA approach enables greater focus on predictor variables with superior forecasting capabilities. Furthermore, we develop a diversity enhancement mechanism for forecast combination by incorporating multiple attention mechanisms, varying numbers of principal components, and a range of forecasting models. The empirical results demonstrates that attention-PCA-based individual forecasting models significantly outperform benchmark models, reducing the Mean Absolute Percentage Error (MAPE) by up to 43.2%. The proposed forecast combination strategy yields the most accurate and diverse forecasts among those evaluated, with the MAPE of the optimal combination model standing at 4.40%.