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

Comparative study of embedding methods.

C J Cellucci1, A M Albano, P E Rapp

  • 1Department of Physics, Ursinus College, Collegeville, Pennsylvania 19426, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 26, 2005
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

Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures.

Cognitive neurodynamics·2014
Same author

Comment on "Performance of different synchronization measures in real data: a case study on electroencephalographic signals".

Physical review. E, Statistical, nonlinear, and soft matter physics·2005
Same author

Statistical validation of mutual information calculations: comparison of alternative numerical algorithms.

Physical review. E, Statistical, nonlinear, and soft matter physics·2005
Same author

Dynamical analysis reveals individuality of locomotion in goldfish.

The Journal of experimental biology·2004
Same author

The algorithmic complexity of multichannel EEGs is sensitive to changes in behavior.

Psychophysiology·2003
Same author

Screening for anxiety and depression in early adolescence.

Journal of the American Academy of Child and Adolescent Psychiatry·2001
Same journal

Tension on dsDNA bound to ssDNA-RecA filaments may play an important role in driving efficient and accurate homology recognition and strand exchange.

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Amplitude-phase coupling drives chimera states in globally coupled laser networks [Phys. Rev. E 91, 040901(R) (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Shapes of sedimenting soft elastic capsules in a viscous fluid [Phys. Rev. E 92, 033003 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Erratum: Attenuation of excitation decay rate due to collective effect [Phys. Rev. E 90, 022142 (2014)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Role of connectivity and fluctuations in the nucleation of calcium waves in cardiac cells [Phys. Rev. E 92, 052715 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
Same journal

Publisher's Note: Lattice Boltzmann approach for complex nonequilibrium flows [Phys. Rev. E 92, 043308 (2015)].

Physical review. E, Statistical, nonlinear, and soft matter physics·2016
See all related articles

Determining optimal embedding parameters for time series analysis requires more than just the data itself. Global false nearest neighbors and mutual information best identified embedding dimension and lag, respectively, in this study.

Area of Science:

  • Dynamical systems analysis
  • Time series analysis
  • Nonlinear dynamics

Background:

  • Embedding experimental data is a critical first step in dynamical analysis.
  • Selecting appropriate embedding parameters (dimension and lag) is essential for successful analysis.
  • Criteria solely based on the time series itself may not determine optimal embedding.

Purpose of the Study:

  • To assess methods for determining optimal embedding parameters for time series.
  • To compare analytical results with time series analysis for validation.
  • To evaluate the robustness of embedding criteria to noise.

Main Methods:

  • Analysis of systems with explicit analytic representations.
  • Comparison of analytical results with time series embedding.

Related Experiment Videos

  • Assessment using global false nearest neighbors for dimension.
  • Assessment using mutual information for lag.

    • Main Results:

    • Optimal embedding parameters cannot be solely determined from the time series.
    • Global false nearest neighbors effectively identified the embedding dimension.
    • Mutual information function successfully identified the optimal lag value.
    • Robustness to noise was considered in the assessment.

    Conclusions:

    • For the considered examples, global false nearest neighbors is recommended for embedding dimension.
    • Mutual information is recommended for identifying the optimal lag value in time series.
    • Limitations of the study are explicitly stated, emphasizing the context of the findings.