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基于经验模式分解和极限学习机的铀资源价格预测方法

DOI: 10.13195/j.kzyjc.2013.0539, PP. 1187-1192

Keywords: 铀资源价格,经验模式分解,固有模态函数,相空间重构,极限学习机,组合预测

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Abstract:

针对国际铀资源价格预测问题,提出一种基于经验模式分解(EMD)、相空间重构(PSR)和极限学习机(ELM)的非线性组合预测方法.首先通过EMD分解,将原始价格序列分解为若干固有模态分量(IMF),按频率高低将各IMF分组叠加成3个新序列;然后在重构相空间的基础上构建不同的ELM模型,分别对各IMF序列进行预测;最后对预测结果进行合成.将该方法应用于实际铀资源价格预测,并与径向基神经网络(RBF)方法及单独ELM方法进行比较,仿真结果表明该方法预测精度有明显的提高.

References

[1]  Huang N E, Wu M L, Qu W L, et al. Applications of Hilbert-Huang transform to non-stationary financial time series analysis[J]. Applied Stochastic Models in Business and Industry, 2003,19(3): 246-268.
[2]  Huang N E. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[C]. Proc of the Royal Society of London Series A — Mathematical Physical and Engineering Sciences. London, 1998, 454(1971): 903-995.
[3]  玄兆燕, 杨公训. 经验模态分解法在大气时间序列预测中的应用[J]. 自动化学报, 2008, 34(1): 97-101.
[4]  (Xuan Z Y, Yang G X. Application of EMD in the atmosphere time series prediction[J]. Acta Automatica Sinica, 2008, 34(1): 97-101.)
[5]  王军栋, 齐维贵. 基于EMD-SVM的江水浊度预测方法研究[J]. 电子学报, 2009, 37(10): 2130-2133.
[6]  (Wang J D, Qi W G. Prediction of river water turbidity based on EMD-SVM[J]. Acta Electronica Sinica, 2009, 37(10): 2130-2133.)
[7]  杨云飞, 鲍玉昆, 胡忠义, 等. 基于EMD和SVMs 的原油价格预测方法[J]. 管理学报, 2010, 7(12): 1884-1889.
[8]  (Yang Y F, Bao Y K, Hu Z Y, et al. Crude oil price prediction based on empirical mode decomposition and support vector machines[J]. Chinese J of Management, 2010, 7(12): 1884-1889.)
[9]  叶林, 刘鹏. 基于经验模态分解和支持向量机的短期风电功率组合预测模型[J]. 中国电机工程学报, 2011, 31(31): 102-108.
[10]  (Ye L, Liu P. Combined model based on EMD-SVM for short-term wind power prediction[J]. Proc of the CSEE, 2011, 31(31): 102-108.)
[11]  Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: Theory and applications[J]. Neurocomputing, 2006, 70(1/2/3): 489-501.
[12]  袁琦, 周卫东, 李淑芳, 等. 基于ELM和近似熵的脑电信号检测方法[J], 仪器仪表学报, 2012, 33(3): 514-519.
[13]  (Yuan Q, Zhou W D, Li S F, et al. Approach of EEG detection based on ELM and approximate entropy[J]. Chinese J of Scientific Instrument, 2012, 33(3): 514-519.)
[14]  李彬, 李贻斌. 基于ELM学习算法的混沌时间序列预测[J]. 天津大学学报, 2011, 44(8): 701-704.
[15]  (Li B, Li Y B. Chaotic time series prediction based on ELM learning algorithm[J]. J of Tianjin University, 2011, 44(8): 701-704.)
[16]  Fraser A M, Swinney H L. Idependent coordinates for strange attractors from mutual information[J]. Physica Review A: General Physics, 1986, 33(2): 1134-1140.
[17]  Grassberger P, Procaccia L. Measuring the strangeness if strange attractors[J]. Physica D, 1983, 9(1/2): 189-208.
[18]  KennelMB, Brown R, Abarbanel H D I, et al. Determining embedding dimension for phase-space reconstruction using a geometrical construction[J]. Physical Review A, 1992, 45(6): 3403-3411.
[19]  Cao L Y. Practical method for determining the minimum embedding dimension of a scalar time series[J]. Physica D, 1997, 110(1/2): 43-50.
[20]  张华强, 张晓燕. 基于混沌理论和LSSVM 的蒸汽负荷预测[J]. 系统工程理论与实践, 2013, 33(4): 1058-1066.
[21]  (Zhang H Q, Zhang X Y. Steam load forecasting based on chaos theory and LSSVM[J]. Systems Engineering-Theory & Practice, 2013, 33(4): 1058-1066.)
[22]  Yu L A, Wang S Y, Lai K K. A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates[J]. Computer & Operation Research, 2005, 32(10): 2523-2541.

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