Random dynamical models from time series

Details Random dynamical models from time series Random dynamical models from time series

Mar 2012

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2012phrve..85c6216m&link_type=abstract

Physical Review E, vol. 85, Issue 3, id. 036216

Statistics

Computation

Nonlinear Dynamics And Chaos, Computational Methods In Statistical Physics And Nonlinear Dynamics, Time Series Analysis, Time Variability

Scientific paper

In this work we formulate a consistent Bayesian approach to modeling stochastic (random) dynamical systems by time series and implement it by means of artificial neural networks. The feasibility of this approach for both creating models adequately reproducing the observed stationary regime of system evolution, and predicting changes in qualitative behavior of a weakly nonautonomous stochastic system, is demonstrated on model examples. In particular, a successful prognosis of stochastic system behavior as compared to the observed one is illustrated on model examples, including discrete maps disturbed by non-Gaussian and nonuniform noise and a flow system with Langevin force.

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