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

Time-Series Graph00:54

Time-Series Graph

4.5K
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...
4.5K
Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
2.3K
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

114
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,...
114
Linear time-invariant Systems01:23

Linear time-invariant Systems

334
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...
334
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

283
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
283
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

336
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
336

You might also read

Related Articles

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

Sort by
Same author

GVA-BLS: Gaussian Vector Autoregression Broad Learning System Based on Randomly Distributed Embedding for Multistep-Ahead Prediction.

IEEE transactions on neural networks and learning systems·2026
Same author

Electromagnetic Wave Absorption Performance of Co<sub>3</sub>Fe<sub>7</sub>/Melamine Sponge Carbon Composites.

Langmuir : the ACS journal of surfaces and colloids·2025
Same author

Grouped Vector Autoregression Reservoir Computing Based on Randomly Distributed Embedding for Multistep-Ahead Prediction.

IEEE transactions on neural networks and learning systems·2025
Same author

Deep belief improved bidirectional LSTM for multivariate time series forecasting.

Mathematical biosciences and engineering : MBE·2023
Same author

Bearing-Fault Diagnosis with Signal-to-RGB Image Mapping and Multichannel Multiscale Convolutional Neural Network.

Entropy (Basel, Switzerland)·2022
Same author

Distinct optical and kinetic responses from E/Z isomers of caspase probes with aggregation-induced emission characteristics.

Journal of materials chemistry. B·2020

Related Experiment Video

Updated: Aug 20, 2025

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.1K

DAFA-BiLSTM: Deep Autoregression Feature Augmented Bidirectional LSTM network for time series prediction.

Heshan Wang1, Yiping Zhang1, Jing Liang1

  • 1College of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 18, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep autoregression feature augmented bidirectional LSTM network (DAFA-BiLSTM) for improved time series forecasting. The DAFA-BiLSTM model effectively captures complex temporal dependencies, outperforming conventional methods in real-world applications.

Keywords:
Deep recurrent neural networkFeature augmentedLong short-term memoryTime series predictionVector autoregression transformation

More Related Videos

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K

Related Experiment Videos

Last Updated: Aug 20, 2025

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.1K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.6K

Area of Science:

  • Machine Learning
  • Time Series Analysis
  • Deep Learning

Background:

  • Time series forecasting is crucial for real-world applications, but conventional models struggle with complex temporal dependencies and spatial correlations.
  • Long short-term memory (LSTM) networks offer improved sequential data handling, yet shallow architectures limit their ability to extract transient characteristics from long intervals.
  • Existing methods often fail to fully exploit latent spatial dependence between variables, leading to suboptimal forecasting performance.

Purpose of the Study:

  • To propose a novel deep bidirectional LSTM architecture, the deep autoregression feature augmented bidirectional LSTM (DAFA-BiLSTM) network, for enhanced time series prediction.
  • To improve the extraction of transient characteristics and spatial dependencies in sequential datasets.
  • To demonstrate the superiority and robustness of the proposed DAFA-BiLSTM model in diverse real-world time series forecasting tasks.

Main Methods:

  • A vector autoregression (VA) transformation module is employed to represent time-delayed linear and nonlinear properties of input signals in an unsupervised manner.
  • Learned nonlinear vectors from VA are progressively fed into multiple BiLSTM layers.
  • Outputs from preceding BiLSTM layers are augmented with time-delayed VA vectors to create enhanced input signals for subsequent layers.

Main Results:

  • The proposed DAFA-BiLSTM model demonstrates superior performance and robustness compared to conventional time series forecasting methods.
  • Extensive experiments on real-world datasets validate the model's effectiveness in capturing complex temporal dynamics.
  • Statistical analysis confirms the DAFA-BiLSTM's adaptive performance, even in noisy environmental conditions.

Conclusions:

  • The novel DAFA-BiLSTM architecture effectively addresses the limitations of conventional time series models by better exploiting spatial and temporal dependencies.
  • The proposed model offers significant improvements in forecasting accuracy and robustness, making it suitable for complex real-world scenarios.
  • DAFA-BiLSTM provides a powerful new tool for time series prediction, particularly for datasets with long intervals and noisy characteristics.