title: Distinguishing dynamics using recurrence-time statistics.
abstract: The probability densities of the mean recurrence time, which is the average time needed for a system to recur to a previously visited neighborhood, are investigated in various dynamical regimes and are found to be in agreement with those of the finite-time Lyapunov exponents. The important advantages of the former ones are that they are easy to estimate and that comparable short time series are sufficient. Asymmetric distributions with exponential tails are observed for intermittency and crisis-induced intermittency, while for typical chaos, the distribution has a Gaussian shape. Further, the shape of the distribution distinguishes intermittent strange nonchaotic attractors from those appearing through fractalization and tori collision mechanisms. Furthermore, statistics performed on the peaks in the frequency distribution of recurrence times unveil scaling behavior in agreement with that obtained from the spectral distribution function defined as the number of peaks in the Fourier spectrum greater than a predefined value. The results of the present recurrence statistics are of relevance in classifying different dynamics and providing important insights into the dynamics of a system when only one realization of this system is available. The practical use of this approach for experimental data is shown on experimental electrochemical time series.
authors.affiliation: Potsdam Institute for Climate Impact Research, Telegraphenberg A 31, 14473 Potsdam, Germany.
authors.forename: E J
authors.initials: EJ
authors.lastname: Ngamga
authors.forename: D V
authors.initials: DV
authors.lastname: Senthilkumar
authors.forename: A
authors.initials: A
authors.lastname: Prasad
authors.forename: P
authors.initials: P
authors.lastname: Parmananda
authors.forename: N
authors.initials: N
authors.lastname: Marwan
authors.forename: J
authors.initials: J
authors.lastname: Kurths
mesh_terms: D019327:Copper Sulfate / Q000737:chemistry; D004563:Electrochemistry; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D011336:Probability; D013464:Sulfuric Acids / Q000737:chemistry; D013997:Time Factors
chemical_list: D013464:Sulfuric Acids; D019327:Copper Sulfate; C033158:sulfuric acid