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Open AccessArticle

Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

by Kaijian He 1,2,*, Rui Zha 1, Jun Wu 1 and Kin Keung Lai 3,4
1
School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China
2
Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong
3
International Business School, Shaanxi Normal University, Xi’an 710119, China
4
Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong
*
Author to whom correspondence should be addressed.
Academic Editor: Bing Wang
Sustainability 2016, 8(4), 387; https://doi.org/10.3390/su8040387
Received: 23 February 2016 / Accepted: 8 April 2016 / Published: 21 April 2016
Recent empirical studies reveal evidence of the co-existence of heterogeneous data characteristics distinguishable by time scale in the movement crude oil prices. In this paper we propose a new multivariate Empirical Mode Decomposition (EMD)-based model to take advantage of these heterogeneous characteristics of the price movement and model them in the crude oil markets. Empirical studies in benchmark crude oil markets confirm that more diverse heterogeneous data characteristics can be revealed and modeled in the projected time delayed domain. The proposed model demonstrates the superior performance compared to the benchmark models. View Full-Text
Keywords: empirical mode decomposition (EMD); multivariate EMD analysis; crude oil price forecasting; time delay embedding; multiscale analysis; ARMA model empirical mode decomposition (EMD); multivariate EMD analysis; crude oil price forecasting; time delay embedding; multiscale analysis; ARMA model
MDPI and ACS Style

He, K.; Zha, R.; Wu, J.; Lai, K.K. Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price. Sustainability 2016, 8, 387.

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