Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sampling Continuous Time Signal

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

Basic Continuous Time Signals

Basic Continuous Time Signals

Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...

Poisson Probability Distribution

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...

Basic Discrete Time Signals

Basic Discrete Time Signals

The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...

Rapidly Varying Flow

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

Construction of Frequency Distribution

Construction of Frequency Distribution

A frequency distribution table can be constructed using the steps given below.
First, make a table with two columns—one with the title of the data that needs to be organized, and the other column for frequency. [Draw a third column for tally marks if needed]. Then, take a look at the items given in the data set and decide if an ungrouped frequency distribution table or a grouped frequency distribution table would be more suitable. If there are large sets of different values, then it is...

You might also read

Related Articles

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

Sort by

Same author

Analysis framework for higher-order temporal correlations with applications to human heartbeats.

Scientific reports·2026

Same author

Hierarchical organization of bursty trains in event sequences.

Chaos (Woodbury, N.Y.)·2025

Same author

Block-corrected modularity for community detection.

Physical review. E·2025

Same author

Carbon-Free Ammonia Co-firing in Fluidized Bed Combustion: Analysis of Advanced Chemical Modeling and Detailed Mechanism.

ACS omega·2025

Same author

Strength and weakness of disease-induced herd immunity in networks.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

Maximum likelihood estimation of burst-merging kernels for bursty time series.

Physical review. E·2025

Search research articles

Related Experiment Video

Updated: Jan 6, 2026

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

Published on: May 9, 2021

Copula-based algorithm for generating bursty time series.

Hang-Hyun Jo^1,2,3, Byoung-Hwa Lee^1,2, Takayuki Hiraoka¹

¹Asia Pacific Center for Theoretical Physics, Pohang 37673, Republic of Korea.

Physical Review. E

|October 3, 2019

Summary

This summary is machine-generated.

Researchers developed a new algorithm using Farlie-Gumbel-Morgenstern copulas to generate event sequences with correlated interevent times (IETs). This method accurately models bursty temporal patterns and outperforms existing techniques for heavy-tailed IET distributions.

More Related Videos

Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One α-Synuclein Monomer at a Time

Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One α-Synuclein Monomer at a Time

Published on: May 30, 2021

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Related Experiment Videos

Last Updated: Jan 6, 2026

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

Contribution of the Na+/K+ Pump to Rhythmic Bursting, Explored with Modeling and Dynamic Clamp Analyses

Published on: May 9, 2021

Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One α-Synuclein Monomer at a Time

Utilizing Time-Resolved Protein-Induced Fluorescence Enhancement to Identify Stable Local Conformations One α-Synuclein Monomer at a Time

Published on: May 30, 2021

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

Area of Science:

Complex Systems
Statistical Modeling
Time Series Analysis

Background:

Many natural and social phenomena exhibit non-Poissonian, bursty temporal patterns.
Temporal correlations in these bursty time series arise from heterogeneous interevent times (IETs) and memory effects between IETs.
Accurate modeling of these processes requires generating event sequences with heavy-tailed IET distributions and memory effects.

Purpose of the Study:

To propose a novel algorithm for generating event sequences with correlated interevent times (IETs).
To enable realistic modeling and simulation of dynamical processes with bursty temporal patterns.

Main Methods:

Development of a Farlie-Gumbel-Morgenstern copula-based algorithm.
The algorithm generates event sequences with specified IET distributions and memory coefficients.
Application and comparison with the existing shuffling method.

Main Results:

Successful generation of event sequences with heavy-tailed IET distributions and correlated IETs.
Demonstrated superior performance of the copula-based algorithm over the shuffling method in certain cases.
Validation of the algorithm's ability to capture memory effects.

Conclusions:

The proposed copula-based algorithm provides a robust method for generating event sequences with correlated IETs.
This approach facilitates more realistic modeling of complex dynamical systems.
The algorithm is expected to advance research in fields exhibiting bursty temporal dynamics.