A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting

Ling Tang (), Wei Dai (), Lean Yu () and Shouyang Wang ()
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Ling Tang: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, P. R. China
Wei Dai: School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, P. R. China
Shouyang Wang: Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, P. R. China

International Journal of Information Technology & Decision Making (IJITDM), 2015, vol. 14, issue 01, 141-169

Abstract: To enhance the prediction accuracy for crude oil price, a novel ensemble learning paradigm coupling complementary ensemble empirical mode decomposition (CEEMD) and extended extreme learning machine (EELM) is proposed. This novel method is actually an improved model under the effective "decomposition and ensemble" framework, especially for nonlinear, complex, and irregular data. In this proposed method, CEEMD, a current extension from the competitive decomposition family of empirical mode decomposition (EMD), is first applied to divide the original data (i.e., difficult task) into a number of components (i.e., relatively easy subtasks). Then, EELM, a recently developed, powerful, fast and stable intelligent learning technique, is implemented to predict all extracted components individually. Finally, these predicted results are aggregated into an ensemble result as the final prediction using simple addition ensemble method. With the crude oil spot prices of WTI and Brent as sample data, the empirical results demonstrate that the novel CEEMD-based EELM ensemble model statistically outperforms all listed benchmarks (including typical forecasting techniques and similar ensemble models with other decomposition and ensemble tools) in prediction accuracy. The results also indicate that the novel model can be used as a promising forecasting tool for complicated time series data with high volatility and irregularity.

Keywords: Crude oil price forecasting; decomposition and ensemble; complementary ensemble empirical mode decomposition (CEEMD); extended extreme learning machine (EELM); 22E46; 53C35; 57S20 (search for similar items in EconPapers)
Date: 2015
References: View complete reference list from CitEc
Citations: View citations in EconPapers (49)

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DOI: 10.1142/S0219622015400015

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