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A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices
Joint Authors
Li, Taiyong
Zhou, Yingrui
Shi, Jiayi
Qian, Zijie
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-03
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Crude oil is one of the most important types of energy for the global economy, and hence it is very attractive to understand the movement of crude oil prices.
However, the sequences of crude oil prices usually show some characteristics of nonstationarity and nonlinearity, making it very challenging for accurate forecasting crude oil prices.
To cope with this issue, in this paper, we propose a novel approach that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme gradient boosting (XGBOOST), so-called CEEMDAN-XGBOOST, for forecasting crude oil prices.
Firstly, we use CEEMDAN to decompose the nonstationary and nonlinear sequences of crude oil prices into several intrinsic mode functions (IMFs) and one residue.
Secondly, XGBOOST is used to predict each IMF and the residue individually.
Finally, the corresponding prediction results of each IMF and the residue are aggregated as the final forecasting results.
To demonstrate the performance of the proposed approach, we conduct extensive experiments on the West Texas Intermediate (WTI) crude oil prices.
The experimental results show that the proposed CEEMDAN-XGBOOST outperforms some state-of-the-art models in terms of several evaluation metrics.
American Psychological Association (APA)
Zhou, Yingrui& Li, Taiyong& Shi, Jiayi& Qian, Zijie. 2019. A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices. Complexity،Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1131811
Modern Language Association (MLA)
Zhou, Yingrui…[et al.]. A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices. Complexity No. 2019 (2019), pp.1-15.
https://search.emarefa.net/detail/BIM-1131811
American Medical Association (AMA)
Zhou, Yingrui& Li, Taiyong& Shi, Jiayi& Qian, Zijie. A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices. Complexity. 2019. Vol. 2019, no. 2019, pp.1-15.
https://search.emarefa.net/detail/BIM-1131811
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1131811