Application Study on Reconstruction of Chaotic Time Series and Prediction of Shanghai Stock Index

Jun Hai MA;Er Shi QI;Xin MO

Systems Engineering - Theory & Practice ›› 2003, Vol. 23 ›› Issue (12) : 86-94.

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PDF(231 KB)
Systems Engineering - Theory & Practice ›› 2003, Vol. 23 ›› Issue (12) : 86-94. DOI: 10.12011/1000-6788(2003)12-86
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Application Study on Reconstruction of Chaotic Time Series and Prediction of Shanghai Stock Index

  • Jun Hai MA ,Er Shi QI ,Xin MO
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Abstract

Higher accurate parameters identification can be met by method of associating neural net-work with wavelet theory, where nonlinear auto-correlated chaotic model is applied. Effect of recon-struction and prediction can be improved by pre-treatment of the chaotic time series and use of Fourier wave filter. In this paper, we establish non linear auto-correlated chaotic models with data of opening, maximum, minimum and closing prices for stock coded 600062 of Shanghai Security market as well as i-dentify the parameters. As it can been seen, results of prediction are comparably accurate.

Key words

nonlinear auto-correlated chaotic model / wavelet neural network / stock data of Shanghai Security Market / parameter identification / time series prdiction

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Jun Hai MA , Er Shi QI , Xin MO. Application Study on Reconstruction of Chaotic Time Series and Prediction of Shanghai Stock Index. Systems Engineering - Theory & Practice, 2003, 23(12): 86-94 https://doi.org/10.12011/1000-6788(2003)12-86
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