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...

Noncompartmental Analysis: Mean Residence Time

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...

Noncompartmental Analysis: Statistical Moment Theory

Noncompartmental Analysis: Statistical Moment Theory

Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...

You might also read

Related Articles

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

Sort by

Same author

A novel electroencephalography-based tool for objective assessment of network dynamics activated by nociceptive stimuli.

European journal of pain (London, England)·2015

Same author

Network dynamics predict improvement in working memory performance following donepezil administration in healthy young adults.

NeuroImage·2013

Same author

Introducing a novel approach of network oriented analysis of ERPs, demonstrated on adult attention deficit hyperactivity disorder.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2012

Same author

Review article: gastric electrical stimulation for gastroparesis--physiological foundations, technical aspects and clinical implications.

Alimentary pharmacology & therapeutics·2009

Same author

ScaleNet--multiscale neural-network architecture for time series prediction.

IEEE transactions on neural networks·2008

Same author

The use of gastric electrical signals for algorithm for automatic eating detection in dogs.

Neurogastroenterology and motility·2008

Search research articles

Related Experiment Video

Updated: Jul 7, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Nonstationary time series analysis by temporal clustering.

S Policker¹, A B Geva

¹Dept. of Electr. & Comput. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|February 5, 2008

Summary

This study introduces a new model and algorithms for analyzing nonstationary time series with changing regimes. It uses fuzzy clustering to estimate continuous drift, improving time series analysis.

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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

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: Jul 7, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

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

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:

Time Series Analysis
Machine Learning
Statistical Modeling

Background:

Nonstationary time series exhibit changing statistical properties over time.
Estimating parameters in time series with regime shifts is challenging.
Existing methods may not adequately capture continuous distributional drifts.

Purpose of the Study:

To present a novel model and algorithms for estimating parameters in nonstationary time series.
To apply fuzzy clustering for estimating continuous drift in time series distributions.
To develop a new cluster validity criterion for temporal patterns.

Main Methods:

Fuzzy clustering applied to time series data.
Interpretation of temporal membership matrix as weights in a time-varying mixture probability distribution function (PDF).
Analysis of algorithm stopping conditions to derive a novel cluster validity criterion.

Main Results:

Successful estimation of parameters for nonstationary time series with continuous regime change.
Demonstration of algorithm performance across three distinct signal types.
Development of a new validity criterion for fuzzy clustering of temporal data.

Conclusions:

The proposed model and algorithms effectively handle nonstationary time series with regime shifts.
Fuzzy clustering offers a robust approach for estimating continuous distributional drift.
The novel cluster validity criterion enhances the analysis of temporal patterns.