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 Concept Videos

SFG Algebra01:16

SFG Algebra

In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from the...

You might also read

Related Articles

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

Sort by
Same author

An Entropy-Based Framework for Hybrid Coalitions in Game Theory-Part I: Human Arbitration.

Entropy (Basel, Switzerland)·2026
Same author

Entropic and algebraic transcript-based tools in time series analysis.

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

Transcript-based estimators for characterizing interactions.

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

Permutation-Based Distances for Groups and Group-Valued Time Series.

Entropy (Basel, Switzerland)·2025
Same author

Nonlinear Dynamics and Applications.

Entropy (Basel, Switzerland)·2025
Same author

Applications of Entropy in Data Analysis and Machine Learning: A Review.

Entropy (Basel, Switzerland)·2025

Related Experiment Video

Updated: May 23, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Transcripts: an algebraic approach to coupled time series.

José M Amigó1, Roberto Monetti, Thomas Aschenbrenner

  • 1Centro de Investigación Operativa, Universidad Miguel Hernandez, Avda. de la Universidad s/n, 03202 Elche, Spain. jm.amigo@umh.es

Chaos (Woodbury, N.Y.)
|April 3, 2012
PubMed
Summary
This summary is machine-generated.

Ordinal symbolic dynamics, using ordinal patterns, offers robust time series analysis. This study explores the algebraic structure of ordinal patterns, introducing new complexity indicators for coupled dynamics.

More Related Videos

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Related Experiment Videos

Last Updated: May 23, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

Area of Science:

  • Complexity Science
  • Time Series Analysis
  • Symbolic Dynamics

Background:

  • Ordinal symbolic dynamics, utilizing ordinal patterns, is increasingly popular for time series analysis due to its robustness against noise and low computational cost.
  • Key tools include permutation entropy, forbidden patterns, and established mathematical results, appealing to both theoreticians and practitioners.
  • The algebraic structure of ordinal patterns remains a less explored but potentially valuable aspect.

Purpose of the Study:

  • To investigate the algebraic structure of ordinal patterns within symbolic dynamics.
  • To generalize the concept of transcripts for N symbolic representations and derive their properties.
  • To introduce and evaluate novel complexity indicators for coupled dynamics based on these transcripts.

Main Methods:

  • Revisiting and generalizing the concept of transcripts between symbolic representations.
  • Developing new complexity indicators derived from the algebraic properties of ordinal patterns.
  • Testing the performance of the proposed indicators using both numerical simulations and real-world datasets.

Main Results:

  • Established general properties of generalized transcripts for N symbolic representations.
  • Defined two novel complexity indicators for coupled dynamics based on the algebraic structure of ordinal patterns.
  • Demonstrated the efficacy of these indicators through application to diverse datasets.

Conclusions:

  • The algebraic structure of ordinal patterns provides a powerful framework for analyzing complex systems.
  • The developed complexity indicators offer a new tool for quantifying coupled dynamics in time series.
  • This research expands the applicability of ordinal symbolic dynamics to a broader range of analytical problems.