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

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 nu...

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Main Authors: Qisheng Yan, Shitong Wang, Bingqing Li
Format: Article
Language:English
Published: Wiley 2014-01-01
Series:Discrete Dynamics in Nature and Society
Online Access:http://dx.doi.org/10.1155/2014/390579
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author Qisheng Yan
Shitong Wang
Bingqing Li
author_facet Qisheng Yan
Shitong Wang
Bingqing Li
author_sort Qisheng Yan
collection DOAJ
description 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.
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institution Kabale University
issn 1026-0226
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language English
publishDate 2014-01-01
publisher Wiley
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series Discrete Dynamics in Nature and Society
spelling doaj-art-98d6bcad9a7b4303a990fd2c97d9ab6a2025-02-03T05:46:40ZengWileyDiscrete Dynamics in Nature and Society1026-02261607-887X2014-01-01201410.1155/2014/390579390579Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space ReconstructionQisheng Yan0Shitong Wang1Bingqing Li2School of Digital Media, Jiangnan University, Wuxi 214122, ChinaSchool of Digital Media, Jiangnan University, Wuxi 214122, ChinaSchool of Science, East China Institute of Technology, Nanchang 330013, ChinaA 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.http://dx.doi.org/10.1155/2014/390579
spellingShingle Qisheng Yan
Shitong Wang
Bingqing Li
Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction
Discrete Dynamics in Nature and Society
title Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction
title_full Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction
title_fullStr Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction
title_full_unstemmed Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction
title_short Forecasting Uranium Resource Price Prediction by Extreme Learning Machine with Empirical Mode Decomposition and Phase Space Reconstruction
title_sort forecasting uranium resource price prediction by extreme learning machine with empirical mode decomposition and phase space reconstruction
url http://dx.doi.org/10.1155/2014/390579
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AT shitongwang forecastinguraniumresourcepricepredictionbyextremelearningmachinewithempiricalmodedecompositionandphasespacereconstruction
AT bingqingli forecastinguraniumresourcepricepredictionbyextremelearningmachinewithempiricalmodedecompositionandphasespacereconstruction