Jove
Visualize
Contact Us

Related Concept Videos

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

31.7K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
31.7K
Ranks01:02

Ranks

428
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
428
Time-Series Graph00:54

Time-Series Graph

4.9K
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...
4.9K
Probability Distributions01:32

Probability Distributions

11.6K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
11.6K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

452
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
452
Random Variables01:09

Random Variables

17.1K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.1K

You might also read

Related Articles

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

Sort by
Same author

Interpretable liquid crystal phase classification via two-by-two ordinal patterns.

Physical review. E·2026
Same author

Gender shapes the relationship between productivity and journal prestige in science.

Scientific reports·2026
Same author

Comprehensive indicators and fine granularity refine density scaling laws in rural-urban systems.

Scientific reports·2026
Same author

Evolving spatiotemporal patterns and urban scaling of deaths from external causes.

Scientific reports·2026
Same author

Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems.

Scientific reports·2025
Same author

Structural roles and gender disparities in corruption networks.

Scientific reports·2025
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 Video

Updated: Jan 3, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

Characterizing stochastic time series with ordinal networks.

Arthur A B Pessa1, Haroldo V Ribeiro1

  • 1Departamento de Física, Universidade Estadual de Maringá-Maringá, PR 87020-900, Brazil.

Physical Review. E
|November 28, 2019
PubMed
Summary
This summary is machine-generated.

Ordinal networks offer a simple yet efficient method for analyzing complex time series data. This study explores their application to various stochastic processes, showing improved noise robustness and accurate Hurst exponent estimation.

More Related Videos

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Related Experiment Videos

Last Updated: Jan 3, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.2K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Area of Science:

  • Complex Systems Analysis
  • Network Science
  • Time Series Analysis

Background:

  • Characterizing complex systems requires effective time series analysis methods.
  • Ordinal networks are a recent, efficient algorithm for time series mapping.
  • Understanding ordinal networks for simple stochastic processes is limited.

Purpose of the Study:

  • Investigate ordinal network properties for diverse time series.
  • Evaluate ordinal networks' robustness against noise and their ability to detect nonrandom behavior.
  • Assess ordinal networks for Hurst exponent estimation and seismic activity analysis.

Main Methods:

  • Generated ordinal networks from random time series, noisy periodic signals, fractional Brownian motion, and earthquake data.
  • Developed an approach for constructing the adjacency matrix of ordinal networks from random series.
  • Estimated local entropy and compared its noise robustness with permutation entropy.
  • Utilized ordinal networks for Hurst exponent estimation and analysis of seismic event series.

Main Results:

  • An exact adjacency matrix construction for random series was presented, aiding in detecting nonrandomness.
  • Local entropy in ordinal networks demonstrated superior noise robustness compared to permutation entropy.
  • Ordinal networks achieved accuracy comparable to state-of-the-art methods for Hurst exponent estimation.
  • Ordinal networks showed potential in identifying sudden changes in seismic activity.

Conclusions:

  • Ordinal networks are versatile tools applicable to a range of stochastic time series.
  • Their robustness to noise and accuracy in parameter estimation highlight their practical utility.
  • Ordinal networks offer a promising approach for analyzing complex systems and detecting critical events in seismic data.