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

Dimensional Analysis01:23

Dimensional Analysis

Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
Dimensional Analysis02:19

Dimensional Analysis

The concept of dimension is important because every mathematical equation linking physical quantities must be dimensionally consistent, implying that mathematical equations must meet the following two rules. The first rule is that, in an equation, the expressions on each side of the equal sign must have the same dimensions. This is fairly intuitive since we can only add or subtract quantities of the same type (dimension). The second rule states that, in an equation, the arguments of any of the...
Dimensional Analysis01:27

Dimensional Analysis

Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena.
In fluid mechanics, dimensional...
Dimensional Analysis03:40

Dimensional Analysis

Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
Time-Series Graph00:54

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...
Problem Solving: Dimensional Analysis01:08

Problem Solving: Dimensional Analysis

Every mathematical equation that connects separate distinct physical quantities must be dimensionally consistent, which implies it must abide by two rules. For this reason, the concept of dimension is crucial. The first rule is that an equation's expressions on either side of an equality must have the exact same dimension, i.e., quantities of the same dimension can be added or removed. The second rule stipulates that all popular mathematical functions, such as exponential, logarithmic, and...

You might also read

Related Articles

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

Sort by
Same author

Methylation profiling of normal tissue adjacent to breast tumors reveals two distinct groups with divergent tumor microenvironment features.

NPJ breast cancer·2026
Same author

Single base focal hypermutation cooccurs with structural variation as an early event in advanced prostate tumourigenesis with ancestry specific independence: a multi-ancestral observational study.

Genome medicine·2026
Same author

CuiT is a Cu importer required for metal homeostasis in <i>Salmonella enterica</i>.

Biochemistry and biophysics reports·2026
Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·2026
Same author

Comprehensive repertoire of the chromosomal alteration and mutational signatures across 16 cancer types.

Nature genetics·2026
Same author

Nutrition as a missing piece in the development of youth male soccer players: a scoping review and future directions.

Biology of sport·2026
Same journal

Invaders taking over-Mollusc faunal change in volcanic barrier lakes of the Albertine Rift biodiversity hotspot.

PloS one·2026
Same journal

AI-driven molecular diversification and ligand-based optimization of macitentan derivatives targeting VEGFR1 and endothelin signaling pathways.

PloS one·2026
Same journal

Performance patterns and records in the world aquatics masters championships: Where do the most frequently represented nations among the top-ten masters swimmers come from?

PloS one·2026
Same journal

Modeling diurnal Temperature-Rainfall relationships under multicollinearity using PLS-SEM: A case study of Ghana.

PloS one·2026
Same journal

Organizational culture, social capital, and emergency capacity in primary healthcare institutions: A cross-sectional structural equation modeling study comparing ordinary and older communities.

PloS one·2026
Same journal

Impact of kidney function on the metabolome in the general population.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 14, 2026

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

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

Published on: June 9, 2023

A method for comparing multivariate time series with different dimensions.

Avraam Tapinos1, Pedro Mendes

  • 1School of Computer Science and Manchester Institute of Biotechnology, The University of Manchester, Manchester, United Kingdom.

Plos One
|February 9, 2013
PubMed
Summary
This summary is machine-generated.

We developed SMETS, a new method for comparing multivariate time series, even when they have different dimensions. This tool aids in understanding complex systems like biochemical networks and financial portfolios.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: May 14, 2026

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

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

Published on: June 9, 2023

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Dynamical Systems Analysis
  • Computational Biology
  • Financial Modeling

Background:

  • Comparing dynamical systems is crucial for understanding their mechanisms and external influences.
  • Existing methods for time series comparison are limited to those with identical dimensions.
  • Multivariate time series are common in characterizing complex systems.

Purpose of the Study:

  • To introduce a novel semi-metric, SMETS, for comparing multivariate time series with potentially different dimensions.
  • To provide a flexible tool for analyzing the behavior of diverse dynamical systems.

Main Methods:

  • Developed a semi-metric (SMETS) that accommodates time series of varying dimensions.
  • Applied SMETS to compare dynamic models of biochemical networks.
  • Utilized SMETS to analyze portfolios of shares, treating them as black-box systems.

Main Results:

  • SMETS successfully compares time series data from systems with differing dimensionalities.
  • Demonstrated the utility of SMETS in both known-dependency (biochemical networks) and black-box (financial portfolios) scenarios.
  • The method provides a robust approach for behavioral comparison of complex systems.

Conclusions:

  • SMETS offers a significant advancement in comparing multivariate time series, overcoming dimensionality constraints.
  • The method is versatile, applicable to both mechanistic models and empirical data.
  • This facilitates deeper insights into the relationships and behaviors of complex dynamical systems.