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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 390579, 10 pages
http://dx.doi.org/10.1155/2014/390579
Research Article

Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction

1School of Digital Media, Jiangnan University, Wuxi 214122, China
2School of Science, East China Institute of Technology, Nanchang 330013, China

Received 28 August 2013; Revised 28 December 2013; Accepted 31 December 2013; Published 20 February 2014

Academic Editor: Wei-Der Chang

Copyright © 2014 Qisheng Yan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.