A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices
Joint Authors
Aslam, Adnan
Xu, Peng
Aamir, Muhammad
Shabri, Ani
Ishaq, Muhammad
Li, Li
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-23, 23 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-10-06
Country of Publication
Egypt
No. of Pages
23
Main Subjects
Abstract EN
Accurate forecasting for the crude oil price is important for government agencies, investors, and researchers.
To cope with this issue, in this paper, a new paradigm is designed for the reconstruction of intrinsic mode functions (IMFs) of decomposition and ensemble models to reduce the complexity in computation and to enhance the forecasting accuracy.
Decomposition and ensemble methodologies significantly enhance the forecasting accuracy under the framework of “divide and conquer” with the proposed reconstruction of IMFs method.
The proposed approach used the autocorrelation at lag 1 of all IMFs for the reconstruction.
The ensemble empirical mode decomposition (EEMD) technique is employed to decompose the data into different IMFs.
Models that utilized the decomposed data relatively perform well, as compared to its application to the undecomposed data.
However, sometimes, the decomposition may produce poor results due to the error accumulation at the end.
Thus, in this study, the reconstruction of IMFs is proposed for minimizing the aforementioned error, thereby increasing the forecasting accuracy.
The Brent and West Texas Intermediate (WTI) datasets (daily and weekly) are exploited to compare the forecasting performance of autoregressive integrated moving average (ARIMA) along with artificial neural network (ANN) models with the decomposed data.
The results have proven that the new paradigm of reconstruction of IMFs through autocorrelation was a better and simple strategy that significantly improved the performance of single models including ARIMA and ANN.
Hence, it is concluded that the proposed model takes less computational time and achieved higher forecasting accuracy with the reconstruction of IMFs as opposed to using all IMFs.
American Psychological Association (APA)
Xu, Peng& Aamir, Muhammad& Shabri, Ani& Ishaq, Muhammad& Aslam, Adnan& Li, Li. 2020. A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-23.
https://search.emarefa.net/detail/BIM-1193135
Modern Language Association (MLA)
Xu, Peng…[et al.]. A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices. Mathematical Problems in Engineering No. 2020 (2020), pp.1-23.
https://search.emarefa.net/detail/BIM-1193135
American Medical Association (AMA)
Xu, Peng& Aamir, Muhammad& Shabri, Ani& Ishaq, Muhammad& Aslam, Adnan& Li, Li. A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-23.
https://search.emarefa.net/detail/BIM-1193135
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1193135