A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting

• A novel decomposition-and-ensemble forecasting model is built for crude oil price.
• The powerful data decomposition of EEMD is utilized to simplify the complex data.
• The effective and stable AI tool of EELM is employed to ensure prediction accuracy.
• Case study compares this model with typical techniques and similar ensemble models.
• The model is shown superior in terms of high accuracy, time saving and robustness.

As one of the most important energy resources, an accurate prediction for crude oil price can effectively guarantee a rapid new production development with higher production quality and less production cost. Accordingly, a novel decomposition-and-ensemble learning paradigm integrating ensemble empirical mode decomposition (EEMD) and extended extreme learning machine (EELM) is proposed for crude oil price forecasting, based on the principle of “decomposition and ensemble”. This novel learning model makes contribution to literature by introducing the current powerful artificial intelligent (AI) technique of EELM in the ensemble model formulation. In the proposed method, EEMD, a competitive decomposition method, is first applied to divide the original data of crude oil price time series into a number of relatively regular components, for simplicity. Second, EELM, a currently proposed, powerful, effective and stable forecasting tool, is implemented to predict all components independently. Finally, these predicted results are aggregated into an ensemble result as final prediction, using simple addition ensemble method. For illustration and verification purposes, the proposed learning paradigm is used to predict the crude oil spot price of WTI. Empirical results demonstrate that the proposed novel ensemble learning paradigm statistically outperforms all considered benchmark models (including popular single models and similar ensemble models) in both prediction accuracy (in terms of level and directional measurement) and effectiveness (in terms of time saving and robustness), indicating that it is a promising tool to predict complicated time series with high volatility and irregularity.