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
Instrument Transformers01:23

Instrument Transformers

141
Instrument transformers, comprising voltage transformers (VTs) and current transformers (CTs), play crucial roles in power substations by providing isolated replicas of current or voltage for measurement and protection purposes. Voltage transformers reduce the primary voltage to levels suitable for relay operation and measurement, while current transformers scale down the primary current. The primary winding of a current transformer often consists of a single turn, achieved by threading the...
141
Transformers in Distribution System01:27

Transformers in Distribution System

156
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
156
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

205
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
205
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

116
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
116
Three-Winding Transformers01:19

Three-Winding Transformers

309
Three identical single-phase transformers can be configured to form a three-phase transformer connection, which involves high-voltage and low-voltage windings. The high-voltage windings are denoted by capital letters A-B-C, while the low-voltage windings are labeled with lowercase letters a-b-c, representing their respective phases. This notation helps distinguish between the high and low voltage sides of the transformer.
In the per-unit equivalent circuit of a grounded Y-Y three-phase...
309

You might also read

Related Articles

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

Sort by
Same author

Construction of Highly Active Zn<sub>3</sub>In<sub>2</sub>S<sub>6</sub> (110)/g-C<sub>3</sub>N<sub>4</sub> System by Low Temperature Solvothermal for Efficient Degradation of Tetracycline under Visible Light.

International journal of molecular sciences·2022
Same author

Robust Template Adjustment Siamese Network for Object Visual Tracking.

Sensors (Basel, Switzerland)·2021
Same author

Self-assembly of peptide amphiphiles for drug delivery: the role of peptide primary and secondary structures.

Biomaterials science·2017
Same author

Ultrasensitive, high-dynamic-range and broadband strain sensing by time-of-flight detection with femtosecond-laser frequency combs.

Scientific reports·2017
Same author

MicroRNAs and complex diseases: from experimental results to computational models.

Briefings in bioinformatics·2017
Same author

Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model.

Journal of translational medicine·2017

Related Experiment Video

Updated: Sep 11, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K

Time series transformer for tourism demand forecasting.

Siyuan Yi1, Xing Chen2, Chuanming Tang3

  • 1Chengdu University of Technology, Chengdu, 610059, Sichuan, China.

Scientific Reports
|August 12, 2025
PubMed
Summary

This study introduces a time series Transformer (Tsformer) for tourism demand forecasting, improving accuracy and interpretability. The Tsformer effectively captures both long-term and short-term dependencies, outperforming existing AI methods.

Keywords:
Deep learningInterpretabilitySearch engine indexTourism demand forecasting

More Related Videos

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K
Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

12.1K

Related Experiment Videos

Last Updated: Sep 11, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.8K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K
Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping
09:48

Fiber Optic Distributed Sensors for High-resolution Temperature Field Mapping

Published on: November 7, 2016

12.1K

Area of Science:

  • Artificial Intelligence
  • Time Series Analysis
  • Tourism Management

Background:

  • AI methods are prevalent in tourism demand forecasting.
  • Existing AI models struggle with long-term dependencies and lack interpretability.
  • Accurate forecasting is crucial for the tourism industry.

Purpose of the Study:

  • To propose a novel AI-based model for tourism demand forecasting.
  • To address limitations in capturing long-term dependencies and interpretability of current methods.
  • To enhance the accuracy of short-term and long-term tourism demand predictions.

Main Methods:

  • Development of a time series Transformer (Tsformer) model.
  • Utilizing an Encoder-Decoder architecture within the Tsformer.
  • Incorporating a calendar feature to merge data points within the forecast horizon.

Main Results:

  • Tsformer demonstrated superior performance over nine baseline methods.
  • The model achieved high accuracy in both short-term and long-term forecasting.
  • Forecasting performance improved with the inclusion of the calendar feature.
  • The model showed effectiveness both pre- and post-COVID-19 outbreak.

Conclusions:

  • The proposed Tsformer offers a more accurate and interpretable alternative for tourism demand forecasting.
  • The Encoder-Decoder architecture effectively captures temporal dependencies.
  • Integrating calendar information enhances forecasting capabilities.