Ordinal patterns in the Duffing oscillator: Analyzing powers of characterization
Ivan Gunther, Arjendu K. Pattanayak, Andrés Aragoneses
Abstract
Ordinal patterns are a time-series data analysis tool used as a preliminary step to construct the permutation entropy, which itself allows the same characterization of dynamics as chaotic or regular as more theoretical constructs such as the Lyapunov exponent. However, ordinal patterns store strictly more information than permutation entropy or Lyapunov exponents. We present results working with the Duffing oscillator showing that ordinal patterns reflect changes in dynamical symmetry that is invisible to other measures, even permutation entropy. We find that these changes in symmetry at given parameter values are correlated with a change in stability at neighboring parameters, which suggests a novel predictive capability for this analysis technique.