Articles | Volume 23, issue 3
Nonlin. Processes Geophys., 23, 137–141, 2016
https://doi.org/10.5194/npg-23-137-2016
Nonlin. Processes Geophys., 23, 137–141, 2016
https://doi.org/10.5194/npg-23-137-2016

Brief communication 10 Jun 2016

Brief communication | 10 Jun 2016

Brief Communication: Breeding vectors in the phase space reconstructed from time series data

Erin Lynch1, Daniel Kaufman2, A. Surjalal Sharma3, Eugenia Kalnay1,4,5, and Kayo Ide1,4,5,6 Erin Lynch et al.
  • 1Department of Atmospheric and Oceanic Science, University of Maryland, College Park, College Park, MD 20742, USA
  • 2Virginia Institute of Marine Science, College of William & Mary, Gloucester Point, VA 23062, USA
  • 3Department of Astronomy, University of Maryland, College Park, College Park, MD 20742, USA
  • 4Institute for Physical Science and Technology, University of Maryland, College Park, College Park, MD 20742, USA
  • 5Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, MD 20742, USA
  • 6Center for Scientific Computation and Mathematical Modeling, University of Maryland, College Park, College Park, MD 20742, USA

Abstract. Bred vectors characterize the nonlinear instability of dynamical systems and so far have been computed only for systems with known evolution equations. In this article, bred vectors are computed from a single time series data using time-delay embedding, with a new technique, nearest-neighbor breeding. Since the dynamical properties of the standard and nearest-neighbor breeding are shown to be similar, this provides a new and novel way to model and predict sudden transitions in systems represented by time series data alone.

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In this article, bred vectors are computed from a single time series data using time-delay embedding, with a new technique, nearest-neighbor breeding. Since the dynamical properties of the nearest-neighbor bred vectors are shown to be similar to bred vectors computed using evolution equations, this provides a new and novel way to model and predict sudden transitions in systems represented by time series data alone.