期刊文献+

小波包与贝叶斯LS-SVM在石油价格预测中的应用 被引量:6

下载PDF
导出
摘要 掌握国际石油价格变化趋势,可以为决策者提供决策依据。文章提出了基于小波包和贝叶斯推断的最小二乘支持向量机石油价格预测方案。对石油价格时间序列进行小波包分解与重构,采用贝叶斯推断对得到的各近似序列和各细节序列进行最小二乘支持向量机模型参数优化,再分别利用优化了的模型进行预测,合成得到最终预测结果。对美国纽约商品交易所原油价格进行仿真实验,结果表明该方法很好地改善了石油价格预测模型的运行速度与预测精度。
出处 《统计与决策》 CSSCI 北大核心 2011年第6期162-164,共3页 Statistics & Decision
  • 相关文献

参考文献8

二级参考文献19

  • 1罗成汉.基于MATLAB神经网络工具箱的BP网络实现[J].计算机仿真,2004,21(5):109-111. 被引量:127
  • 2Farooq M,Mahdi N.Forecasting output using oil prices:a cascaded artificial neural network approach[J]Journal of Economics and Business,2006,58(2) : 168-180.
  • 3Gori F,Ludovisi D,Cerritelli P F.Forecast of oil price and consumption in the short term under three scenarios:parabolic,linear and chaotic behaviour[J].Energy,2007,32(7):1291-1296.
  • 4Mirmirani S,Li H C.A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil[J].Advances in Econometrics, 2004,19 : 203-223.
  • 5Vapnik V N.The nature of statistical learning theory[M].New York: Springer-Verlag, 1995.
  • 6Suykens J K,Gestel T.Least squares support vector machines[M]. Singapore: World Scientifics, 2002.
  • 7Suykens J K, De Brabanter J,Lukas L.Weighted least squares support vector machines:robustness and sparse approximation[J].Neurocomputing, 2002,48 ( 1 ) : 85-105.
  • 8Vapnik V. The Nature of Statistical Learning Theory[ M ].Berlin : Springer-Verlag, 1995.
  • 9Suykens J A K, Van Gestel T, de Moor B, et al. Least Squares Support Vector Machines [ M ]. Singapore: World Scientific, 2002.
  • 10Van Gestel T, Suykens J A K, Baestaens D K,et al. Financial time series prediction using least squares support vector machines within the evidence framework [ J ]. IEEE Transactions on Neural Network, 2001 , 12(4) : 809 821.

共引文献87

同被引文献68

  • 1唐振鹏,张婷婷,吴俊传,杜晓旭,陈凯杰.基于混合模型的原油价格多步预测研究[J].计量经济学报,2021(2):346-361. 被引量:7
  • 2梁强,范英,魏一鸣.基于小波分析的石油价格长期趋势预测方法及其实证研究[J].中国管理科学,2005,13(1):30-36. 被引量:52
  • 3Yu L, 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: 2523-2541.
  • 4Chaffari A, Zare S. A novel algorithm for prediction of crude oil price variation based on soft computing[J]. Energy Economics, 2009, 31(4): 531- 536.
  • 5He L T, Hu C, Casey K M. Prediction of variability in mortgage rates: Interval computing solutions[J]. The Journal of Risk Finance, 2009, 10(2): 142- 154.
  • 6Nguyen H T, Nabney L T. Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models[J]. Energy, 2010, 35(9): 3674-3685.
  • 7Reboredo J C, Rivera-Castro M A. A wavelet decomposition approach to crude oil price and exchange rate dependence[J]. Economic Modelling, 2013(32): 42-57.
  • 8Kao L J, Chiu C C, Lu C J, et al. A hybrid approach by integrating wavelet-based feature extraction with MARS and SVR for stock index forecasting[J]. Decision Support Systems, 2013(54): 1228-1244.
  • 9Tang M M, Zhang J L. A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices[J]. Journal of Economics and Business, 2012(64): 275-286.
  • 10Huang S C. Forecasting stock indices with wavelet domain kernel partial least square regressions[J]. Applied Soft Computing, 2011(11): 5433-5443.

引证文献6

二级引证文献76

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部