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

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

140
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
140
Hindsight Biases01:12

Hindsight Biases

3.9K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
3.9K
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

177
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
177
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

296
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
296
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

123
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
123
Survival Tree01:19

Survival Tree

154
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...
154

You might also read

Related Articles

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

Sort by
Same author

The structural grammar of integration and competition in the human connectome.

Frontiers in computational neuroscience·2026
Same author

Integrated anatomical and functional connectivity mapping in episodic migraine: a spectral graph theory approach.

Scientific reports·2026
Same author

Shifts in brain dynamics and drivers of consciousness state transitions.

Frontiers in computational neuroscience·2026
Same author

[The role of EEG in tomorrow's medicine].

Lakartidningen·2024
Same author

Spike sorting in the presence of stimulation artifacts: a dynamical control systems approach.

Journal of neural engineering·2024
Same author

The role of long-term power-law memory in controlling large-scale dynamical networks.

Scientific reports·2023

Related Experiment Video

Updated: Sep 6, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

10.8K

A time-reversed model selection approach to time series forecasting.

Max Sibeijn1, Sérgio Pequito2

  • 1Delft Center for Systems and Control, Delft University of Technology, Delft, The Netherlands. m.w.sibeijn@tudelft.nl.

Scientific Reports
|June 28, 2022
PubMed
Summary

We introduce the backwards validated information criterion (BVIC) for time series forecasting model selection. The BVIC balances model fit with its ability to predict backwards, showing comparable performance to existing methods and improved accuracy in determining true model order.

More Related Videos

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K
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

1.5K

Related Experiment Videos

Last Updated: Sep 6, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

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

Published on: December 9, 2015

10.8K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K
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

1.5K

Area of Science:

  • Time Series Analysis
  • Statistical Modeling
  • Machine Learning

Background:

  • Traditional model selection in time series forecasting often relies on information criteria.
  • Linear stationary processes, like AR processes, exhibit time-reversibility where direction is independent of parameters.
  • A need exists for theoretically grounded, data-driven model selection methods.

Purpose of the Study:

  • To introduce a novel model selection criterion for time series forecasting.
  • To develop a criterion that leverages time-reversibility principles and backwards prediction (backcasting).
  • To evaluate the performance of the new criterion against conventional methods.

Main Methods:

  • Developed the backwards validated information criterion (BVIC).
  • Combined theoretical time-reversibility with information criteria principles.
  • Tested BVIC on synthetic and real-world datasets, comparing it with existing criteria.
  • Assessed time series order in epileptic seizure data using BVIC.

Main Results:

  • BVIC demonstrated comparable forecasting performance to conventional information criteria.
  • No statistically significant differences in forecast error were found in most experiments.
  • BVIC showed superior accuracy in recovering the true order of the underlying time series process.
  • Model order was observed to increase during epileptic events in electrocorticographic data.

Conclusions:

  • The BVIC offers a theoretically sound and data-driven approach to time series model selection.
  • Its ability to accurately determine model order provides an advantage over static penalization methods.
  • BVIC shows promise for analyzing complex real-world data, such as neurological signals.