Forecast of Chaotic Series in a Horizon Superior to the Inverse of the Maximum Lyapunov Exponent

Miguel Alfaro, Guillermo Fuertes, Manuel Vargas, Juan Sepúlveda and Matias Veloso-Poblete

Complexity, 2018, vol. 2018, 1-9

Abstract:

In this article, two models of the forecast of time series obtained from the chaotic dynamic systems are presented: the Lorenz system, the manufacture system, and the volume of the Great Salt Lake of Utah. The theory of the nonlinear dynamic systems indicates the capacity of making good-quality predictions of series coming from dynamic systems with chaotic behavior up to a temporal horizon determined by the inverse of the major Lyapunov exponent. The analysis of the Fourier power spectrum and the calculation of the maximum Lyapunov exponent allow confirming the origin of the series from a chaotic dynamic system. The delay time and the global dimension are employed as parameters in the models of forecast of artificial neuronal networks (ANN) and support vector machine (SVM). This research demonstrates how forecast models built with ANN and SVM have the capacity of making forecasts of good quality, in a superior temporal horizon at the determined interval by the inverse of the maximum Lyapunov exponent or theoretical forecast frontier before deteriorating exponentially.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:1452683

DOI: 10.1155/2018/1452683

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