Predicting chaotic time series with a partial model

Methods for forecasting time series are a critical aspect of the understanding and control of complex networks. When the model of the network is unknown, nonparametric methods for prediction have been developed, based on concepts of attractor reconstruction pioneered by Takens and others. In this Ra...

Full description

Saved in:
Bibliographic Details
Published inPhysical review. E, Statistical, nonlinear, and soft matter physics Vol. 92; no. 1; p. 010902
Main Authors Hamilton, Franz, Berry, Tyrus, Sauer, Timothy
Format Journal Article
LanguageEnglish
Published United States 01.07.2015
Online AccessGet more information

Cover

Loading…
More Information
Summary:Methods for forecasting time series are a critical aspect of the understanding and control of complex networks. When the model of the network is unknown, nonparametric methods for prediction have been developed, based on concepts of attractor reconstruction pioneered by Takens and others. In this Rapid Communication we consider how to make use of a subset of the system equations, if they are known, to improve the predictive capability of forecasting methods. A counterintuitive implication of the results is that knowledge of the evolution equation of even one variable, if known, can improve forecasting of all variables. The method is illustrated on data from the Lorenz attractor and from a small network with chaotic dynamics.
ISSN:1550-2376
DOI:10.1103/PhysRevE.92.010902