Jove
Visualize
Contact Us

Related Experiment Videos

Forecasting Financial Time Series through Causal and Dilated Convolutional Neural Networks.

Lukas Börjesson1, Martin Singull1

  • 1Department of Mathematics, Linköping University, 581 83 Linköping, Sweden.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

677
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
677

You might also read

Related Articles

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

Sort by
Same author

Asymptotic Normality and Convergence Rates for Tsallis Entropy Estimators via Stabilization Techniques.

Entropy (Basel, Switzerland)·2026
Same author

Improved Dividend Estimation from Intraday Quotes.

Entropy (Basel, Switzerland)·2022
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles
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

This study adapted a speech generation model for financial forecasting, outperforming a base model by over 20% in predicting stock index price movements and trends.

Area of Science:

  • Quantitative Finance
  • Machine Learning Applications
  • Time Series Analysis

Background:

  • Accurate financial market prediction remains challenging due to market complexity and temporal data dependencies.
  • Traditional models often rely on efficient market hypothesis assumptions, which may not fully capture market dynamics.
  • The temporal structure of financial data significantly impacts model performance, requiring careful data handling.

Purpose of the Study:

  • To adapt a novel machine learning model, successful in audio and speech generation, for financial time series prediction.
  • To compare the performance of the adapted model against a naive base model constrained by efficient market assumptions.
  • To evaluate the model's efficacy in predicting future stock index price movements and trends.

Main Methods:

Keywords:
causal and dilated convolutional neural networksdeep learningfinancial time series

Related Experiment Videos

  • A generative model, previously used for audio and speech, was modified for financial data analysis.
  • The model was trained and validated using historical stock index data, considering temporal data splits.
  • Performance was benchmarked against a naive model adhering to efficient market hypothesis principles.

Main Results:

  • The adapted model demonstrated superior predictive performance compared to the naive base model.
  • The model achieved over 20% improvement in predicting the next day's closing price.
  • The model showed a 37% improvement in predicting the next day's price trend.

Conclusions:

  • The adapted generative model shows significant potential for improving financial market prediction accuracy.
  • The study highlights the importance of handling temporal data structures in financial time series analysis.
  • This approach offers a promising alternative to traditional models for forecasting stock index movements.