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

Improving the false nearest neighbors method with graphical analysis.

T Aittokallio1, M Gyllenberg, J Hietarinta

  • 1Department of Applied Mathematics, University of Turku, FIN-20014 Turku, Finland.

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
|April 24, 2002
PubMed
Summary
This summary is machine-generated.

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

Classification of <i>Enterobacteriaceae</i> by minimization of stochastic complexity.

Microbiology (Reading, England)·2021
Same author

Noncanonical effector functions of the T-memory-like T-PLL cell are shaped by cooperative TCL1A and TCR signaling.

Blood·2020
Same author

Corrections: Reinstated p53 response and high anti-T-cell leukemia activity by the novel alkylating deacetylase inhibitor tinostamustine.

Leukemia·2020
Same author

Reinstated p53 response and high anti-T-cell leukemia activity by the novel alkylating deacetylase inhibitor tinostamustine.

Leukemia·2020
Same author

Spoken words are processed during dexmedetomidine-induced unresponsiveness.

British journal of anaesthesia·2018
Same author

Adaptive correlations between seed size and germination time.

Journal of mathematical biology·2018

We present a new graphical method for analyzing false nearest neighbors (FNN). This approach enhances the ability to distinguish deterministic chaos from noise and optimize chaos detection conditions.

Area of Science:

  • Nonlinear dynamics
  • Chaos theory
  • Time series analysis

Background:

  • The standard false nearest neighbors (FNN) method calculates the percentage of false neighbors.
  • This original method does not consider the spatial distribution of neighboring points in time-delay coordinates.
  • Distinguishing deterministic chaos from random noise in dynamical systems remains a challenge.

Purpose of the Study:

  • To introduce a novel graphical presentation for the false nearest neighbors (FNN) method.
  • To improve the ability to differentiate between deterministic chaos and noise.
  • To provide a tool for optimizing parameters for detecting low-dimensional chaos.

Main Methods:

  • Development of a graphical representation for the FNN analysis.

Related Experiment Videos

  • Visualization of the distribution of neighboring points in time-delay phase space.
  • Comparative analysis of the graphical FNN method against the traditional percentage-based FNN.

    • Main Results:

    • The graphical presentation facilitates a clearer distinction between deterministic chaos and noise.
    • The method aids in identifying optimal embedding parameters for chaos detection.
    • Enhanced understanding of the applicability and limitations of the FNN technique.

    Conclusions:

    • The graphical FNN method offers significant advantages over the traditional approach.
    • This visualization tool improves the reliability of chaos identification in time series data.
    • The approach enhances the interpretability and practical application of nonlinear time series analysis.