[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()

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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

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.

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.

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