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

Time-Series Graph

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...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

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...

Survival Tree

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...

Scatter Plot

Scatter Plot

The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:

You might also read

Related Articles

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

Sort by

Same author

Behavioral and Biochemical Evaluation of a Curcumin-Loaded Nano-Liposomal Formulation in a Scopolamine-Induced Mouse Model of Cognitive Impairment.

Biomolecules & therapeutics·2026

Same author

Correction: Kim et al. Identification of <i>GREM-1</i> and <i>GAS6</i> as Specific Biomarkers for Cancer-Associated Fibroblasts Derived from Patients with Non-Small-Cell Lung Cancer. <i>Cancers</i> 2025, <i>17</i>, 2858.

Cancers·2026

Same author

Anbalcabtagene autoleucel (PD-1 and TIGIT knockdown CD19 CAR-T) for relapsed/refractory large B-cell lymphoma (CRC01-01).

Blood·2026

Same author

Fibrinogen concentrates versus cryoprecipitate for intraoperative hypofibrinogenemia in liver transplantation: study protocol for a randomized trial (FIBCRYO-LT trial).

Trials·2026

Same author

Analysis of Tuberculosis Case Incidence and Trends in 2025.

Public health weekly report·2026

Same author

Seroprevalence of Japanese Encephalitis Virus, and Immunogenicity of the Vero-Cell Culture-Derived Vaccine in Hematopoietic Stem Cell Transplantation Recipients.

Journal of Korean medical science·2026

Search research articles

Related Experiment Video

Updated: May 23, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Dynamic Periodic Event Graphs for multivariate time series pattern prediction.

SoYoung Park¹, HyeWon Lee¹, Sungsu Lim¹

¹Department of Computer Science and Engineering, Chungnam National University, Daejeon, Republic of South Korea.

Peerj. Computer Science

|March 10, 2025

Summary

This summary is machine-generated.

This study introduces dynamic Periodic Event Graphs (PEGs) to improve multivariate time series analysis by incorporating data periodicity. PEGs enhance link prediction accuracy by capturing recurring patterns, outperforming existing methods.

Keywords:

Dynamic graphs Event graphs Graph neural networks Link prediction Multivariate time series analysis Self-supervised learning

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Related Experiment Videos

Last Updated: May 23, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Area of Science:

Data Science
Machine Learning
Time Series Analysis

Background:

Complex systems require robust multivariate time series pattern analysis.
Dynamic graph neural networks are effective but often ignore data periodicity.
Overlooking periodicity reduces prediction accuracy in time series.

Purpose of the Study:

To introduce dynamic Periodic Event Graphs (PEGs) for time series analysis.
To enhance prediction accuracy by leveraging inherent data periodicity.
To improve link prediction in multivariate time series.

Main Methods:

Time series decomposition to extract seasonal components and identify representative periods.
Frequency analysis to determine key periodicities within seasonal components.
Extraction of motif patterns (recurring sub-sequences) to define event nodes.
Construction of dynamic bipartite event graphs based on periodic motif patterns.

Main Results:

Demonstrated over 5% improvement in link prediction performance.
Achieved enhanced accuracy in both transductive and inductive learning scenarios.
Validated effectiveness on multiple periodic multivariate time series datasets.

Conclusions:

Dynamic PEGs effectively capture and utilize periodicity in time series data.
The method offers substantial enhancements in predictive accuracy and generalization.
Publicly available code ensures reproducibility and facilitates future research.