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

Transformers01:26

Transformers

1.7K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.7K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

395
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
395
Transformers in Distribution System01:27

Transformers in Distribution System

498
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
498
State Space Representation01:27

State Space Representation

534
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
534
Types Of Transformers01:16

Types Of Transformers

1.4K
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
1.4K
Time-Series Graph00:54

Time-Series Graph

5.0K
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...
5.0K

You might also read

Related Articles

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

Sort by
Same author

Temporal change in liver function after stereotactic body radiation therapy for hepatocellular carcinoma.

Journal of gastrointestinal oncology·2026
Same author

Temporal point process modeling of aggressive behavior onset in psychiatric inpatient youths with autism.

Scientific reports·2026
Same author

Challenging the Paradigm a Symptom-Based Classification Is Sufficient for Identifying Disorders of Gut-Brain Interaction.

The American journal of gastroenterology·2026
Same author

Therapies Targeting Mucosal Eosinophils in Eosinophilic Gut Diseases From Esophagus to Colon.

Journal of gastroenterology and hepatology·2026
Same author

Development and Feasibility Assessment of a Multimodal Digital Health Technology for Remote Monitoring of Symptoms in Myasthenia Gravis.

Digital biomarkers·2025
Same author

Heart Scar-In-A-Dish: Tissue Culture Platform to Study Myocardial Injury and Mechanics In Vitro.

Journal of biomechanical engineering·2025
See all related articles
  1. Home
  3. Dynamics-aware Representation Learning Via Multivariate Time Series Transformers.
  1. Home
  3. Dynamics-aware Representation Learning Via Multivariate Time Series Transformers.

Related Experiment Videos

Dynamics-aware Representation Learning via Multivariate Time Series Transformers.

Michael Potter1, İlkay Yıldız Potter2, Octavia Camps3

  • 1Naval Surface Warfare Center Corona, Norco, CA, USA.

... European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
|September 11, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel transformer autoencoder for multivariate time series analysis, achieving superior forecasting and classification performance on chaotic systems and benchmark datasets.

Related Experiment Videos

Area of Science:

  • Machine Learning
  • Dynamical Systems
  • Time Series Analysis

Background:

  • Multivariate time series analysis is crucial for understanding complex systems.
  • Existing methods often struggle with interpretability and performance on chaotic data.
  • Koopman operator theory offers a powerful framework for analyzing nonlinear dynamics.

Purpose of the Study:

  • To develop a novel multivariate time series autoencoder.
  • To create interpretable, linear-dynamical latent features for downstream tasks.
  • To improve time series forecasting and classification accuracy.

Main Methods:

  • Combined a transformer autoencoder with a dynamical atoms-based autoencoder.
  • Mimicked Koopman operators in the latent space.
  • Utilized dynamics-aware representations with a transformer classifier.

    • Main Results:

    • Significantly outperformed deep Koopman operator learning baselines for forecasting chaotic systems.
    • Achieved state-of-the-art classification accuracy on benchmark multivariate time series datasets.
    • Demonstrated interpretable linear-dynamical latent feature extraction.

    Conclusions:

    • The proposed method offers a powerful approach for multivariate time series analysis.
    • The model enhances interpretability and predictive performance.
    • This work advances the application of Koopman operator theory in machine learning.