Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Jiang Wu, Feng Miu and Taiyong Li
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Jiang Wu: School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
Feng Miu: School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China
Taiyong Li: School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China

Energies, 2020, vol. 13, issue 7, 1-20

Abstract: Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a great challenge for accurately forecasting crude oil prices. In this study, a self-optimizing ensemble learning model incorporating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sine cosine algorithm (SCA), and random vector functional link (RVFL) neural network, namely ICEEMDAN-SCA-RVFL, is proposed to forecast crude oil prices. Firstly, we employ ICEEMDAN to decompose the raw series of crude oil prices into a group of relatively simple subseries. Secondly, RVFL is used to forecast the target values for each decomposed subseries individually. Due to the complex parameter settings of ICEEMDAN and RVFL, SCA is introduced to optimize the parameters for ICEEMDAN and RVFL in the above decomposition and prediction stages simultaneously. Finally, we assemble the predicted values of all individual subseries as the final predicted values of crude oil prices. Our proposed ICEEMDAN-SCA-RVFL significantly outperforms the single and ensemble benchmark models, as demonstrated by a case study conducted using the time series of West Texas Intermediate (WTI) daily crude oil spot prices.

Keywords: crude oil prices forecasting; decomposition and ensemble; improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN); sine cosine algorithm (SCA); random vector functional link (RVFL) neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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