A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting

To enhance prediction accuracy and reduce computation complexity, a decomposition–ensemble methodology with data-characteristic-driven reconstruction is proposed for crude oil price forecasting, based on two promising principles of “divide and conquer” and “data-characteristic-driven modeling”. Actually, this proposed model improves the existing decomposition–ensemble techniques in the “divide and conquer” framework, by formulating and incorporating a data-characteristic-driven reconstruction method based on the “data-characteristic-driven modeling”. Four main steps are involved in the proposed methodology, i.e., data decomposition for simplifying the complex data, component reconstruction based on the “data-characteristic-driven modeling” for capturing inner factors and reducing computational cost, individual prediction for each reconstructed component via a certain artificial intelligence (AI) tool, and ensemble prediction for final output. In the proposed data-characteristic-driven reconstruction, all decomposed modes are thoroughly analyzed to explore the hidden data characteristics, and are accordingly reconstructed into some meaningful components. For illustration and verification, the West Texas Intermediate (WTI) and Brent crude oil spot prices are used as the sample data, and the empirical results indicate that the proposed model statistically outperforms all considered benchmark models (including popular AI single models, typical decomposition–ensemble models without reconstruction, and similar decomposition–ensemble models with other existing reconstruction methods), since it has higher prediction accuracy and less computational time.