Fourier Phase Index for Extracting Signatures of Determinism and Nonlinear Features in Time Series

  • IFISC Seminar

  • Markus Franziskus Müller
  • Centro de Investigación en Ciencias, Universidad Autónoma del Estado de Morelos, México and Centro de Ciencias de la Complejidad, Ciudad de México
  • Nov. 23, 2023, 2:30 p.m.
  • IFISC Seminar Room
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  • Announcement file

Detecting determinism and nonlinear properties from empirical time series is highly nontrivial.

Traditionally, nonlinear time series analysis is based on an error-prone phase space reconstruction

that is only applicable for stationary, largely noise-free data from a low-dimensional system and

requires the nontrivial adjustment of various parameters. We present a data driven index based on

Fourier phases that detects determinism at a well defined significance level, without using Fourier

transform surrogate data. It extracts nonlinear features, is robust to noise, provides time-frequency

resolution by a double running window approach and potentially distinguishes regular and chaotic

dynamics. We test this method on data derived from dynamical models as well as on real world

data, namely intracranial recordings of an epileptic patient and a series of density related variations

of sediments of a paleolake in Tlaxcala, Mexico.

Presential in the seminar room. Zoom link:

Note the unusual day of the week (Thu).

Contact details:

Tobias Galla

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