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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 390579, 10 pages
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.
- J.-J. Wang, J.-Z. Wang, Z.-G. Zhang, and S.-P. Guo, “Stock index forecasting based on a hybrid model,” Omega, vol. 40, no. 6, pp. 758–766, 2012.
- B. Z. Zhu and Y. M. Wei, “Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology,” Omega, vol. 41, pp. 517–524, 2013.
- S.-C. Huang, P.-J. Chuang, C.-F. Wu, and H.-J. Lai, “Chaos-based support vector regressions for exchange rate forecasting,” Expert Systems with Applications, vol. 37, no. 12, pp. 8590–8598, 2010.
- A.-S. Chen, M. T. Leung, and H. Daouk, “Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index,” Computers & Operations Research, vol. 30, no. 6, pp. 901–923, 2003.
- Y. Zhang and L. Wu, “Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network,” Expert Systems with Applications, vol. 36, no. 5, pp. 8849–8854, 2009.
- C.-F. Chen, M.-C. Lai, and C.-C. Yeh, “Forecasting tourism demand based on empirical mode decomposition and neural network,” Knowledge-Based Systems, vol. 26, pp. 281–287, 2012.
- D. P. Mandic and J. Chambers, Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, John Wiley & Sons, 2001.
- H. Jaeger and H. Haas, “Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication,” Science, vol. 304, no. 5667, pp. 78–80, 2004.
- A. Kazem, E. Sharifi, F. K. Hussain, M. Saberi, and O. K. Hussain, “Support vector regression with chaos-based firefly algorithm for stock market price forecasting,” Applied Soft Computing, vol. 13, pp. 947–958, 2013.
- G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006.
- F. L. Chen and T. Y. Ou, “Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry,” Expert Systems with Applications, vol. 38, no. 3, pp. 1336–1345, 2011.
- M. Xia, Y. C. Zhang, L. G. Weng, and X. L. Ye, “Fashion retailing forecasting based on extreme learning machine with adaptive metrics of inputs,” Knowledge-Based Systems, vol. 36, pp. 253–259, 2012.
- C. J. Lu and Y. E. Shao, “Forecasting computer products sales by integrating ensemble empirical mode decomposition and extreme learning machine,” Mathematical Problems in Engineering, vol. 2012, Article ID 831201, 15 pages, 2012.
- Y. K. Bao, T. Xiong, and Z. Y. Hu, “Forecasting air passenger traffic by support vector machines with ensemble empirical mode decomposition and slope-based method,” Discrete Dynamics in Nature and Society, vol. 2012, Article ID 431512, 12 pages, 2012.
- C.-J. Lu, T.-S. Lee, and C.-C. Chiu, “Financial time series forecasting using independent component analysis and support vector regression,” Decision Support Systems, vol. 47, no. 2, pp. 115–125, 2009.
- K. L. Chen, C. C. Yeh, and T. L. Lu, “A hybrid demand forecasting model based on empirical mode decomposition and neural network in TFT-LCD industry,” Cybernetics and Systems, vol. 43, no. 5, pp. 426–441, 2012.
- H. Liu and J. Wang, “Integrating independent component analysis and principal component analysis with neural network to predict chinese stock market,” Mathematical Problems in Engineering, vol. 2011, Article ID 382659, 2011.
- C.-J. Lu, “Integrating independent component analysis-based denoising scheme with neural network for stock price prediction,” Expert Systems with Applications, vol. 37, no. 10, pp. 7056–7064, 2010.
- N. E. Huang, Z. Shen, S. R. Long et al., “The empirical mode decomposition and the Hubert spectrum for nonlinear and non-stationary time series analysis,” Proceedings of the Royal Society A, vol. 454, no. 1971, pp. 903–995, 1998.
- N. E. Huang, M.-L. C. Wu, S. R. Long et al., “A confidence limit for the empirical mode decomposition and Hilbert spectral analysis,” Proceedings of the Royal Society A, vol. 459, no. 2037, pp. 2317–2345, 2003.
- Z.-Y. Xuan and G.-X. Yang, “Application of EMD in the atmosphere time series prediction,” Acta Automatica Sinica, vol. 34, no. 1, pp. 97–101, 2008.
- J.-D. Wang and W.-G. Qi, “Prediction of river water turbidity based on EMD-SVM,” Acta Electronica Sinica, vol. 37, no. 10, pp. 2130–2133, 2009.
- Y. F. Yang, Y. K. Bao, Z. Y. Hu, and R. Zhang, “Crude oil price prediction based on empirical mode decomposition and support vector machines,” Chinese Journal of Management, vol. 7, no. 12, pp. 1884–1889, 2010.
- L. Ye and P. Liu, “Combined model based on EMD-SVM for short-term wind power prediction,” Proceedings of the Chinese Society of Electrical Engineering, vol. 31, no. 31, pp. 102–108, 2011.
- Y. Wei and M.-C. Chen, “Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks,” Transportation Research Part C, vol. 21, no. 1, pp. 148–162, 2012.
- F. Takens, “Detecting strange attractors in turbulence,” in Dynamical Systems and Turbulence, Warwick 1980 (Coventry, 1979/1980), vol. 898 of Lecture Notes in Math., pp. 366–381, Springer, Berlin, Germany, 1981.
- T. Sauer, J. A. Yorke, and M. Casdagli, “Embedology,” Journal of Statistical Physics, vol. 65, no. 3-4, pp. 579–616, 1991.
- T. Gautama, D. P. Mandic, and M. M. Van Hulle, “A differential entropy based method for determining the optimal embedding parameters of a signal,” in Proceedings of the IEEE International Conference on Accoustics, Speech, and Signal Processing, vol. 6, pp. 29–32, April 2003.
- T. Gautama, D. P. Mandic, and M. M. Van Hulle, “The delay vector variance method for detecting determinism and nonlinearity in time series,” Physica D, vol. 190, no. 3-4, pp. 167–176, 2004.
- L. Li and L. Chong-Xin, “Application of chaos and neural network in power load forecasting,” Discrete Dynamics in Nature and Society, vol. 2011, Article ID 597634, 12 pages, 2011.
- A. Wolf, J. B. Swift, H. L. Swinney, and J. A. Vastano, “Determining Lyapunov exponents from a time series,” Physica D, vol. 16, no. 3, pp. 285–317, 1985.
- L. Yu, S. Wang, and K. K. Lai, “A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates,” Computers & Operations Research, vol. 32, no. 10, pp. 2523–2541, 2005.
- X. Zhang, K. K. Lai, and S.-Y. Wang, “A new approach for crude oil price analysis based on Empirical Mode Decomposition,” Energy Economics, vol. 30, no. 3, pp. 905–918, 2008.
- Z. Wu and N. E. Huang, “Ensemble empirical mode decomposition: a noise-assisted data analysis method,” Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1–41, 2009.
- N. Rehman and D. P. Mandic, “Filter bank property of multivariate empirical mode decomposition,” IEEE Transactions on Signal Processing, vol. 59, no. 5, pp. 2421–2426, 2011.
- N. Rehman and D. P. Mandic, “Multivariate empirical mode decomposition,” Proceedings of the Royal Society A, vol. 466, no. 2117, pp. 1291–1302, 2010.
- N. ur Rehman, C. Park, N. E. Huang, and D. P. Mandic, “EMD via MEMD: multivariate noise-aided computation of standard EMD,” Advances in Adaptive Data Analysis. Theory and Applications, vol. 5, no. 2, Article ID 1350007, 25 pages, 2013.
- A. Ahrabian, C. C. Took, and D. P. Mandic, “Algorithmic trading using phase synchronization,” IEEE Journal of Selected Topics in Signal Processing, vol. 6, pp. 399–404, 2012.