A Novel Hybrid Approach with A Decomposition Method and The RVFL Model for Crude Oil Price Prediction

Chengyuan Zhang, Fuxin Jiang, Shouyang Wang, Wei Shang

Abstract: Volatility of international crude oil prices is influenced by various external factors on different time scales. User search data (USD) which reflects investor attentions has been widely researched and proved to be associated with crude oil price change at different frequency bands. In this paper, a novel hybrid approach that utilizes bivariate empirical mode decomposition (BEMD) with user search data and machine learning is developed for crude oil price forecasting. First, BEMD is adopted to simultaneously decomposed the crude oil price data and USD into a finite set of components. Second, each component is modelled and predicted by random vector functional link (RVFL) network and the corresponding final results are obtained via an ensemble model. Third, Brent crude oil spot price is used to test the proposed approach empirically. Forecasting results are analyzed with various evaluation criteria and verified robustness. Results show that the proposed approach statistically outperforms traditional forecasting machine learning techniques and similar counterparts (with USD or EMD-based method) in terms of prediction accuracy.