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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

178
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,...
178
Linear time-invariant Systems01:23

Linear time-invariant Systems

585
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...
585
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

3.4K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
3.4K
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

646
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
646
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.2K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.2K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

220
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....
220

You might also read

Related Articles

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

Sort by
Same author

Introduction.

International journal of neural systems·2025
Same author

Analytical Equations for the Prediction of the Failure Mode of Reinforced Concrete Beam-Column Joints Based on Interpretable Machine Learning and SHAP Values.

Sensors (Basel, Switzerland)·2025
Same author

Explainable Image Similarity: Integrating Siamese Networks and Grad-CAM.

Journal of imaging·2023
Same author

Introduction.

International journal of neural systems·2023
Same author

Special Issue on Machine Learning and AI for Sensors.

Sensors (Basel, Switzerland)·2023
Same author

Introduction.

International journal of neural systems·2022
Same journal

Supporting human-agent communication for explainable planning in spatial-temporal planning problems.

Neural computing & applications·2026
Same journal

Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition.

Neural computing & applications·2026
Same journal

Sequential pattern transformer (SPT): a generative and interpretable framework for predicting disease trajectories.

Neural computing & applications·2026
Same journal

Balancing misclassification errors in image-based inference using problem domain semantics and a nested cascade architecture.

Neural computing & applications·2025
Same journal

Deep multi-objective reinforcement learning for utility-based infrastructural maintenance optimization.

Neural computing & applications·2025
Same journal

A fairness scale for real-time recidivism forecasts using a national database of convicted offenders.

Neural computing & applications·2025
See all related articles

Related Experiment Videos

Smoothing and stationarity enforcement framework for deep learning time-series forecasting.

Ioannis E Livieris1, Stavros Stavroyiannis2, Lazaros Iliadis3

  • 1Department of Mathematics, University of Patras, Patras, 265-00 Greece.

Neural Computing & Applications
|May 10, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new data preprocessing framework to enhance deep learning time-series models. The method improves forecasting performance across various financial and energy datasets.

Keywords:
Deep LearningForecastingStationarityTime-series

Related Experiment Videos

Area of Science:

  • Data Mining
  • Machine Learning
  • Time-Series Analysis

Background:

  • Time-series analysis and forecasting are complex data mining challenges.
  • Deep learning models require high-quality data for effective training and performance.

Purpose of the Study:

  • To propose a novel framework for enhancing deep learning time-series models.
  • To improve the efficiency and accuracy of time-series forecasting and classification.

Main Methods:

  • A two-stage data preprocessing methodology is introduced.
  • Stage 1: Smoothing techniques to create a de-noised time-series.
  • Stage 2: Differencing to ensure stationarity for deep learning model fitting.

Main Results:

  • The framework was tested on cryptocurrency, energy, and stock market datasets.
  • Experiments covered both regression and classification tasks.
  • The proposed framework significantly improved deep learning model forecasting performance.

Conclusions:

  • The data preprocessing framework effectively enhances deep learning time-series models.
  • The methodology generates high-quality time-series data suitable for complex modeling.
  • Empirical evidence supports the framework's ability to improve forecasting accuracy.