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

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 challen...

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Bibliographic Details
Published in:Energies (Basel) 2020-04, Vol.13 (7), p.1852
Main Authors: Wu, Jiang, Miu, Feng, Li, Taiyong
Format: Article
Language:English
Subjects:
Adaptive algorithms
Case studies
Crude oil
Crude oil prices
crude oil prices forecasting
Decomposition
decomposition and ensemble
Economic development
Forecasting
improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)
Methods
Neural networks
Noise
Optimization
random vector functional link (RVFL) neural network
sine cosine algorithm (SCA)
Stochastic models
Support vector machines
Time series
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13071852