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

Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Linear time-invariant Systems01:23

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...
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

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, the...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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...
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
Econometric Views (EViews)01:29

Econometric Views (EViews)

Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...

You might also read

Related Articles

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

Sort by
Same author

Recovery of Platinum Group Metals from Spent Automotive Catalysts: A Review of Processes and Challenges.

Materials (Basel, Switzerland)·2026
Same author

A Network Visualization Query System for Multi-Drug Compatibility Based on a WeChat Mini Program: A Preliminary Usability and Efficiency Evaluation.

JMIR formative research·2026
Same author

Flexible Sensing Technology for Myocardial Tissue Contractile Force With Integrated Magnetically Actuated Mechanical Stimulation.

Advanced healthcare materials·2026
Same author

Sparse Pd-Te Covalent Bridges Drive Anomalous Bulk-to-Monolayer Electronic and Magnetic Evolution in FePd<sub>2</sub>Te<sub>2</sub>.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Corrigendum to "Combined transcriptomics and metabolomics to reveal the effects of copper exposure on the liver of rainbow trout (Oncorhynchus mykiss)" [Ecotoxicol. Environ. Saf. 2024, 284, 116996].

Ecotoxicology and environmental safety·2026
Same author

A Photocurable Conductive Hydrogel for Force Sensing of Cross-Type Muscle Tissue.

ACS applied materials & interfaces·2026
Same journal

Integrated multi-assessment and structural performance index framework for stacking-sequence optimisation of natural fibre reinforced laminates.

Scientific reports·2026
Same journal

SuperiorGAT: graph attention networks for sparse LiDAR point cloud reconstruction in autonomous systems.

Scientific reports·2026
Same journal

The effect of stretching the pectoralis major, sternocleidomastoid, and iliopsoas muscles on 800 m swimming performance in master swimmers.

Scientific reports·2026
Same journal

ISNR-PQC: isometry noise resilience post quantum cryptography primitive.

Scientific reports·2026
Same journal

Identification of high-yielding and stable genotypes of barley in the cold climate of Iran using AMMI and GGE biplot models.

Scientific reports·2026
Same journal

Bayesian negative binomial modelling of spatial and temporal patterns of road traffic deaths in Ghana.

Scientific reports·2026
See all related articles

Related Experiment Videos

Financial time series forecasting with a hybrid VMD-CSA-BiT framework.

Guiyan Zhao1, Jiayuan Ouyang2, Jianhui Yang2

  • 1Qiaoxing School of Economics and Management, Fujian Polytechnic Normal University, Fuzhou, 350300, China. zgy19790925@163.com.

Scientific Reports
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces VMD-CSA-BiT, a novel framework for financial time series forecasting. It significantly improves prediction accuracy and stability for noisy, complex market data.

Keywords:
Bidirectional transformersConvolutional self-attentionFinancial time series forecastingVariational mode decomposition

Related Experiment Videos

Area of Science:

  • Quantitative Finance
  • Computational Economics
  • Machine Learning

Background:

  • Financial time series forecasting is challenged by nonlinearity, non-stationarity, and noise.
  • Existing models often struggle with complex market dynamics and data volatility.

Purpose of the Study:

  • To propose an integrated framework, VMD-CSA-BiT, for robust financial time series forecasting.
  • To enhance prediction accuracy and stability in financial markets.

Main Methods:

  • Variational Mode Decomposition (VMD) to decompose time series into intrinsic mode functions.
  • Convolutional Self-Attention (CSA) to refine time-step representations.
  • Bidirectional Transformers (BiT) to model long-term temporal dependencies.

Main Results:

  • VMD-CSA-BiT demonstrated consistent performance improvements over benchmark models.
  • Achieved significant reductions in key error metrics for financial forecasting.
  • Visual analyses confirmed predictions align well with actual market movements.

Conclusions:

  • VMD-CSA-BiT is a promising and effective approach for financial time series forecasting.
  • The framework offers superior accuracy and stability compared to existing methods.
  • Future work includes architectural optimizations and broader financial applications.