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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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| 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. |
| format | Article |
| id | doaj-art-98d6bcad9a7b4303a990fd2c97d9ab6a |
| institution | Kabale University |
| issn | 1026-0226 1607-887X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| 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 |
| work_keys_str_mv | AT qishengyan forecastinguraniumresourcepricepredictionbyextremelearningmachinewithempiricalmodedecompositionandphasespacereconstruction AT shitongwang forecastinguraniumresourcepricepredictionbyextremelearningmachinewithempiricalmodedecompositionandphasespacereconstruction AT bingqingli forecastinguraniumresourcepricepredictionbyextremelearningmachinewithempiricalmodedecompositionandphasespacereconstruction |