Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Testing for nonlinearity in time series without the Fourier transform.

Tomomichi Nakamura1, Xiaodong Luo, Michael Small

  • 1Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. entomo@eie.polyu.edu.hk

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 31, 2005
PubMed
Summary

This study introduces a novel method for detecting nonlinearity in time series data. The approach bypasses Fourier transforms, offering a more efficient way to analyze complex systems.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A multiscale spatio-temporal convolutional attention network for depressive disorder diagnosis via coarse-grained signal reconstruction based on EEG.

Scientific reports·2026
Same author

Resilience in collective behaviors of "next generation reservoir computer" oscillators via transmitting signal distortion.

Chaos (Woodbury, N.Y.)·2026
Same author

Nonlinear dynamics of reservoir computing: Theory, realization, and application.

Chaos (Woodbury, N.Y.)·2026
Same author

Chaos, computation and the century of complexity.

Chaos (Woodbury, N.Y.)·2026
Same author

Mapping Aboriginal Mental Health Journeys Through Psychiatric Care Systems.

JAMA network open·2026
Same author

From EEG signals to quantitative assessment: predicting depression severity using a novel deep learning framework.

Scientific reports·2026

Area of Science:

  • Time series analysis
  • Nonlinear dynamics
  • Signal processing

Background:

  • Traditional methods for nonlinearity testing often rely on Fourier transforms.
  • Fourier transform-based surrogate techniques can be limited by power spectrum estimation challenges.
  • Distinguishing between linear and nonlinear system behavior is crucial in many scientific fields.

Purpose of the Study:

  • To propose a new method for testing nonlinearity in time series data.
  • To avoid the limitations associated with Fourier transform-based surrogate techniques.
  • To provide a robust algorithm for identifying nonlinear characteristics in stationary time series.

Main Methods:

  • A novel algorithm is developed to test for nonlinearity.
  • The method does not require the application of Fourier transforms.

Related Experiment Videos

  • It leverages the inherent structural differences between linear and nonlinear systems.

    • Main Results:

    • The proposed method successfully identifies nonlinearity in time series.
    • It circumvents the drawbacks of power spectrum estimation inherent in Fourier transform methods.
    • The algorithm effectively distinguishes data generated by linear systems from nonlinear ones.

    Conclusions:

    • A new, efficient method for nonlinearity testing in time series is presented.
    • This approach offers an alternative to existing surrogate techniques, avoiding Fourier transform limitations.
    • The method provides a valuable tool for analyzing the dynamics of stationary systems.