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Determining mixed linear-nonlinear coupled differential equations from multivariate discrete time series sequences. (English) Zbl 1009.37501
Summary: A new method is described for extracting mixed linear-nonlinear coupled differential equations from multivariate discrete time series data. It is assumed in the present work that the solution of the coupled ordinary differential equations can be represented as a multivariate Volterra functional expansion. A tractable hierarchy of moment equations is generated by operating on a suitably truncated Volterra functional expansion. The hierarchy facilitates the calculation of the coefficients of the coupled differential equations.
In order to demonstrate the method’s ability to accurately estimate the coefficients of the governing differential equations, it is applied to data derived from the numerical solution of the Lorenz equations with additive noise.
The method is then used to construct a dynamic global mid- and high-magnetic latitude ionospheric model where nonlinear phenomena such as period doubling and quenching occur. It is shown that the estimated inhomogeneous coupled second-order differential equation model for the ionospheric peak plasma density can accurately forecast the future behaviour of a set of ionosonde stations which encompass the earth. Finally, the method is used to forecast the future behaviour of a portfolio of Japanese common stock prices. The hierarchy method can be used to characterise the observed behaviour of a wide class of coupled linear and mixed linear-nonlinear phenomena.

MSC:
37M10 Time series analysis of dynamical systems
86A10 Meteorology and atmospheric physics
86-08 Computational methods for problems pertaining to geophysics
37N20 Dynamical systems in other branches of physics (quantum mechanics, general relativity, laser physics)
91B28 Finance etc. (MSC2000)
37N40 Dynamical systems in optimization and economics
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