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...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Sequence Networks of Rotating Machines

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...

Linear time-invariant Systems

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Discrete-Time Fourier Series

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...

You might also read

Related Articles

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

Sort by

Same author

A digital archive reveals how a funding agency cooperated with academics to support the nascent field of genomics.

Nature communications·2026

Same author

The evolution of interdisciplinarity and internationalization in scientific journals.

eLife·2025

Same author

Pattern and structural detection in grayscale images through the application of quantile graphs in higher-dimensional spaces.

Scientific reports·2025

Same author

Reply to Neveu and Neveu: Inference in an information-restricted environment.

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

Same author

Reply to Singer: Strike paper mills at the root.

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

Same author

The entities enabling scientific fraud at scale are large, resilient, and growing rapidly.

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

Search research articles

Related Experiment Video

Updated: May 30, 2026

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

Duality between time series and networks.

Andriana S L O Campanharo¹, M Irmak Sirer, R Dean Malmgren

¹Laboratory for Computing and Applied Mathematics, Instituto Nacional de Pesquisas Espaciais, São José dos Campos, São Paulo, Brazil.

|August 23, 2011

Summary

We introduce a novel method to map time series data to networks, enabling analysis of complex systems. This approach allows for reciprocal characterization, enhancing both network and time series analysis.

Related Experiment Videos

Last Updated: May 30, 2026

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:

Complex Systems Science
Network Science
Time Series Analysis

Background:

Understanding system dynamics often involves analyzing component interactions and temporal evolution.
Existing methods map time series to networks, but the relationship between network topology and time series properties remains unclear.

Purpose of the Study:

To develop a novel mapping from time series to networks with an approximate inverse operation.
To enable the use of network statistics for time series characterization and vice versa.

Main Methods:

Proposed a new map from time series to networks with an approximate inverse.
Generated an ensemble of time series data, from periodic to random.
Validated information retention after applying the map and its inverse.

Main Results:

The proposed map successfully retains significant information from the original time series after transformation.
Network analysis effectively distinguishes different dynamic regimes within time series.
Time series analysis provides novel tools to augment traditional network quantification.

Conclusions:

The developed mapping facilitates a bidirectional analysis between time series and networks.
Network analysis can identify distinct dynamic behaviors in time series data.
Time series analysis offers new quantitative methods for network characterization.