Modeling Time Series Data of Real Systems

Details Modeling Time Series Data of Real Systems Modeling Time Series Data of Real Systems

2006-07-14

arxiv.org/abs/nlin/0607027v1

Nonlinear Sciences

Chaotic Dynamics

17 pages, 12 figures, accepted in International Journal of Modern Physics C (IJMPC) in May 2006

Scientific paper

10.1142/S0129183107010474

Dynamics of complex systems is studied by first considering a chaotic time series generated by Lorenz equations and adding noise to it. The trend (smooth behavior) is separated from fluctuations at different scales using wavelet analysis and a prediction method proposed by Lorenz is applied to make out of sample predictions at different regions of the time series. The prediction capability of this method is studied by considering several improvements over this method. We then apply this approach to a real financial time series. The smooth time series is modeled using techniques of non linear dynamics. Our results for predictions suggest that the modified Lorenz method gives better predictions compared to those from the original Lorenz method. Fluctuations are analyzed using probabilistic considerations.

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