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Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction
Joint Authors
Yan, Qisheng
Wang, Shitong
Li, Bingqing
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-20
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
A hybrid forecasting approach combining empirical mode decomposition (EMD), phase space reconstruction (PSR), and extreme learning machine (ELM) for international uranium resource prices is proposed.
In the first stage, the original uranium resource price series are first decomposed into a finite number of independent intrinsic mode functions (IMFs), with different frequencies.
In the second stage, the IMFs are composed into three subseries based on the fine-to-coarse reconstruction rule.
In the third stage, based on phase space reconstruction, different ELM models are used to model and forecast the three subseries, respectively, according to the intrinsic characteristic time scales.
Finally, in the foruth stage, these forecasting results are combined to output the ultimate forecasting result.
Experimental results from real uranium resource price data demonstrate that the proposed hybrid forecasting method outperforms RBF neural network (RBFNN) and single ELM in terms of RMSE, MAE, and DS.
American Psychological Association (APA)
Yan, Qisheng& Wang, Shitong& Li, Bingqing. 2014. Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction. Discrete Dynamics in Nature and Society،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-468333
Modern Language Association (MLA)
Yan, Qisheng…[et al.]. Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction. Discrete Dynamics in Nature and Society No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-468333
American Medical Association (AMA)
Yan, Qisheng& Wang, Shitong& Li, Bingqing. Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction. Discrete Dynamics in Nature and Society. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-468333
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-468333