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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

301
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
301

You might also read

Related Articles

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

Sort by
Same author

Prediction of PM2.5 time series by seasonal trend decomposition-based dendritic neuron model.

Neural computing & applications·2023
Same author

An Extension Network of Dendritic Neurons.

Computational intelligence and neuroscience·2023
Same author

A Dendritic Neuron Model with Adaptive Synapses Trained by Differential Evolution Algorithm.

Computational intelligence and neuroscience·2020
Same author

Mr<sup>2</sup>DNM: A Novel Mutual Information-Based Dendritic Neuron Model.

Computational intelligence and neuroscience·2019
Same author

A Pruning Neural Network Model in Credit Classification Analysis.

Computational intelligence and neuroscience·2018
Same author

Astragalus Polysaccharide Attenuated Iron Overload-Induced Dysfunction of Mesenchymal Stem Cells via Suppressing Mitochondrial ROS.

Cellular physiology and biochemistry : international journal of experimental cellular physiology, biochemistry, and pharmacology·2016
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
Same journal

RETRACTION: Distributed Scheduling Strategy of Virtual Power Plant Using the Particle Swarm Optimization Neural Network under Blockchain Background.

Computational intelligence and neuroscience·2025
See all related articles

Related Experiment Video

Updated: Mar 7, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.5K

Statistical Modeling and Prediction for Tourism Economy Using Dendritic Neural Network.

Ying Yu1, Yirui Wang1, Shangce Gao1

  • 1Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan.

Computational Intelligence and Neuroscience
|March 2, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Seasonal Autoregressive Integrated Moving Average with Dendritic Neural Network (SA-D) model for accurate tourism demand forecasting. The SA-D model significantly improves predictive performance compared to existing methods.

More Related Videos

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K

Related Experiment Videos

Last Updated: Mar 7, 2026

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

7.5K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

6.1K

Area of Science:

  • Economics
  • Computer Science
  • Data Science

Background:

  • Global internationalization drives rapid tourism economy development.
  • Accurate tourism demand forecasting is crucial for economic planning.
  • Existing forecasting methods require innovation to meet growing demands.

Purpose of the Study:

  • To propose an innovative forecasting model for tourism demand.
  • To enhance predictive accuracy in tourism economics.
  • To introduce the Seasonal Autoregressive Integrated Moving Average with Dendritic Neural Network (SA-D) model.

Main Methods:

  • Utilized the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to remove long-term linear trends.
  • Employed a dendritic neural network model to train residual data for short-term predictions.
  • Developed the combined Seasonal Autoregressive Integrated Moving Average with Dendritic Neural Network (SA-D) model.

Main Results:

  • The SA-D model demonstrated considerably better predictive performance.
  • Comparative analysis using data from other studies confirmed the SA-D model's effectiveness.
  • Achieved superior results in normalized mean square error, absolute percentage of error, and correlation coefficient.

Conclusions:

  • The proposed SA-D model offers a significant advancement in tourism demand forecasting.
  • This hybrid approach effectively captures both long-term trends and short-term fluctuations.
  • The SA-D model provides a robust and accurate tool for the tourism industry.