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

Causes of Similarity-Dissimilarity Effect01:26

Causes of Similarity-Dissimilarity Effect

371
The similarity-dissimilarity effect, a fundamental concept in social psychology, explains how interpersonal similarities and differences influence attraction and social interactions. This effect is supported by three key psychological perspectives: balance theory, social comparison theory, and consensual validation.Balance Theory and Cognitive ConsistencyBalance theory, developed by Fritz Heider, posits that individuals seek cognitive consistency in their relationships. When two people share...
371
Time-Series Graph00:54

Time-Series Graph

3.8K
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...
3.8K
Correlation of Experimental Data01:23

Correlation of Experimental Data

588
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
588
Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

624
The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
624
Multiple Comparison Tests01:13

Multiple Comparison Tests

3.4K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
3.4K
Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

1.3K
Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Dual site targeting of the bacterial 70S ribosome by tetracyclines.

Nature communications·2026
Same author

RETRACTED: Effect of transconjunctival sutureless vitrectomy versus 20-G vitrectomy on surgical wound closure in patients: A meta-analysis.

International wound journal·2026
Same author

Model-dependent and model-independent visualization of hydrogen atoms in high-resolution cryoEM maps.

Acta crystallographica. Section D, Structural biology·2026
Same author

Cryo-EM structures of photosystem I with alternative quinones reveals new insight into cofactor selectivity.

bioRxiv : the preprint server for biology·2026
Same author

Computational Evolution of Anti-PD-1 Antibodies Induces Structural Refolding for High-Affinity Interactions.

Biochemistry·2026
Same author

Visualizing the translation landscape in human cells at high resolution.

Nature communications·2025

Related Experiment Video

Updated: Apr 28, 2026

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

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

Published on: June 9, 2023

2.0K

Multivariate time series similarity searching.

Jimin Wang1, Yuelong Zhu1, Shijin Li1

  • 1College of Computer & Information, Hohai University, Nanjing 210098, China.

Thescientificworldjournal
|June 5, 2014
PubMed
Summary
This summary is machine-generated.

A new dimension-combination method enhances multivariate time series (MTS) similarity searches by combining individual dimension similarities. This approach offers advantages over traditional methods for MTS data analysis.

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

21.0K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

Related Experiment Videos

Last Updated: Apr 28, 2026

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

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

Published on: June 9, 2023

2.0K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

21.0K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.3K

Area of Science:

  • Data Science
  • Machine Learning
  • Time Series Analysis

Background:

  • Multivariate time series (MTS) data are prevalent across finance, multimedia, and hydrology.
  • Existing similarity measures may not effectively capture complex patterns in MTS data.

Purpose of the Study:

  • To propose and validate a novel dimension-combination method for MTS similarity searching.
  • To improve the accuracy and effectiveness of similarity searches in MTS datasets.

Main Methods:

  • Calculate similarity for individual dimensions of MTS data.
  • Synthesize single-dimension similarities using a weighted BORDA count for overall MTS similarity.
  • Integrate with existing similarity search techniques.

Main Results:

  • The proposed dimension-combination method demonstrated advantages over traditional measures like Euclidean distance (ED) and dynamic time warping (DTW).
  • Experiments on six UCI KDD Archive datasets showed competitive classification accuracy.
  • Effectiveness varied across datasets, highlighting the need for adaptable measures.

Conclusions:

  • The dimension-combination method provides a valuable new option for MTS similarity searches.
  • No single similarity measure is universally optimal for all MTS datasets.
  • The approach offers a flexible and effective way to handle MTS data complexity.