A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting

This study proposes a new method for crude oil future price forecasting. The original crude oil futures price series is decomposed into a series of sub-sequences using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, and the permutation entropy (PE) method is employed to reconstruct these sub-sequences into high-frequency, low-frequency, and trend components. Using the kernel extreme learning machine (KELM) optimised by the chaotic sparrow search algorithm (CSSA), the low-frequency component and trend component are predicted. However, the high-frequency component is decomposed secondary to the empirical mode decomposition (EMD) method, and the PE and CSSA-KELM models are employed again to obtain the linear integrating prediction result for the high-frequency component. Finally, the forecasting results of the high-frequency, low-frequency, and trend components are nonlinearly integrated with the CSSA-KELM model, and the final forecasting value for crude oil futures prices is obtained. To verify the effectiveness of the proposed model, we empirically forecast the Brent and WTI crude oil futures prices. The empirical results show that the approach proposed in this study improves forecasting accuracy compared to other benchmark models and has good robustness.