Multi-Perspective Crude Oil Price Forecasting with a New Decomposition-Ensemble Framework

Crude oil is an important global commodity, and its price fluctuation affects the political and economic security of a country. Therefore, it is necessary to conduct crude oil price forecasting. This study proposes a multi-perspective crude oil price forecasting model that combines structured and unstructured data under a new decomposition-ensemble framework. Specifically, the crude oil price series is decomposed and reconstructed into several modes through variational mode decomposition (VMD) and fuzzy entropy (FE). Further, we screen the effective predictors from structured and unstructured datasets using the Granger causality test and select the optimal input features through random forest - recursive feature elimination (RF-RFE). Finally, based on different input features, each reconstruction mode is individually forecasted using appropriate forecasting technology, and the final forecasting values are obtained through integrating the forecasting value of each reconstruction mode. The West Texas Intermediate (WTI) daily spot price is adopted to validate the performance of our proposed model. The empirical results show that compared with the benchmark model, the proposed model can significantly improve forecasting performance