[1] Fang Fen, Wang Haiyan,. Local polynomial prediction methodof multivariate chaotic time series and its application [J]. Journal of Southeast University (English Edition), 2005, 21 (2): 229-232. [doi:10.3969/j.issn.1003-7985.2005.02.023]
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Local polynomial prediction methodof multivariate chaotic time series and its application(
)
多变量混沌时间序列局部多项式预测方法及应用
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 21
- Issue:
- 2005 2
- Page:
- 229-232
- Research Field:
- Mathematics, Physics, Mechanics
- Publishing date:
- 2005-06-30
Info
- Title:
- Local polynomial prediction methodof multivariate chaotic time series and its application
- 多变量混沌时间序列局部多项式预测方法及应用
- Author(s):
- Fang Fen, Wang Haiyan
- College of Economics and Management, Southeast University, Nanjing 210096, China
- Keywords:
- chaotic time series; phase space reconstruction; local polynomial prediction; stock market
- PACS:
- O175;O241
- DOI:
- 10.3969/j.issn.1003-7985.2005.02.023
- Abstract:
- To improve the prediction accuracy of chaotic time series, a new method formed on the basis of local polynomial prediction is proposed.The multivariate phase space reconstruction theory is utilized to reconstruct the phase space firstly, and on its basis, a polynomial function is applied to construct the prediction model, then the parameters of the model according to the data matrix built with the embedding dimensions are estimated and a one-step prediction value is calculated.An estimate and one-step prediction value is calculated.Finally, the mean squared root statistics are used to estimate the prediction effect.The simulation results obtained by the Lorenz system and the prediction results of the Shanghai composite index show that the local polynomial prediction errors of the multivariate chaotic time series are small and its prediction accuracy is much higher than that of the univariate chaotic time series.
- 为了改善混沌时间序列的预测精度, 提出了一种新的多变量混沌时间序列的局部多项式预测方法.它首先利用多变量时间序列的相空间重构理论重构相空间, 并据此利用多项式函数构造预测模型, 该模型根据嵌入维数构造数据矩阵, 进行模型的参数估计和计算一步预测值, 最后根据平均根统计量推断预测效果.Lorenz系统的模拟仿真和上海综合股价指数的局部预测结果表明:用多变量混沌时间序列局部多项式预测法进行预测的误差小, 且比单变量混沌时间序列局部多项式预测法的预测精度高.
References:
[1] Wang Haiyan, Zhu Mei.A prediction comparison between univariate and multivariate chaotic time series [J].Journal of Southeast University (English Edition), 2003, 19(4):414-417.
[2] Farmer J D, Sidorowich J J.Exploiting chaos to predict the future and reduce noise [A].In:Lee Y C, ed.Evolution, Learning and Cognition[C].River Edge:World Scientific, 1987.277-330.
[3] Kasim K, Levent S, Orhan S.Nonlinear time series prediction of O3 concentration in Istanbul [J].Atmospheric Environment, 2000, 34(8):1267-1271.
[4] Wang Haiyan, Sheng Zhaohan, Zhang Jin.Phase space reconstruction of complex system based on multivariate time series [J].Journal of Southeast University (Natural Science Edition), 2003, 33(1):115-118.(in Chinese)
[5] Cao Liangyue, Mee A, Judd K.Dynamics from multivariate time series [J].Physica D, 1998, 121(1, 2):75-88.
[6] Kantz H, Schreiber T.Nonlinear time series analysis [M].Cambridge:Cambridge University Press, 1997.127-133.
[7] Fraser A M, Swinney H L.Independent coordinates for strange attractors from mutual information [J].Phys Rev A, 1986, 33(2):1134-1140.
[8] Gao Hongbing, Pan Jin, Cheng Hongmin.Judgment of chaos in our securities market [J].System Engineering, 2001, 18(6):28-32.(in Chinese)
Memo
- Memo:
- Biographies: Fang Fen(1977—), female, graduate;Wang Haiyan(corresponding author), male, doctor, associate professor, hywang@seu.edu.cn.