Extracting unstable periodic orbits from chaotic time series data

P So, E Ott, T Sauer, B J Gluckman, C Grebogi, S J Schiff

Research output: Contribution to journalArticlepeer-review

110 Citations (Scopus)


A general nonlinear method to extract unstable periodic orbits from chaotic time series is proposed. By utilizing the estimated local dynamics along a trajectory, we devise a transformation of the time series data such that the transformed data are concentrated on the periodic orbits. Thus, one can extract unstable periodic orbits from a chaotic time series by simply looking for peaks in a finite grid approximation of the distribution function of the transformed data. Our method is demonstrated using data from both numerical and experimental examples, including neuronal ensemble data from mammalian brain slices. The statistical significance of the results in the presence of noise is assessed using surrogate data.

Original languageEnglish
Pages (from-to)5398-5417
Number of pages20
JournalPhysical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
Issue number5
Publication statusPublished - May 1997




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