Regional tourism demand forecasting with spatiotemporal interactions: a multivariate decomposition deep learning model
With the advancement of economic globalization and regional integration, regional tourism flows are more closely linked, which provides new clues for improving forecasting. This study develops a multivariate decomposition deep learning model to forecast tourism demand by capturing spatiotemporal int...
Main Authors: | Yang, Dongchuan, Li, Yanzhao, Guo, Ju’e, Li, Guang, Sun, Shaolong |
---|---|
Other Authors: | Nanyang Business School |
Format: | Journal Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/173592 |
Similar Items
-
Inbound Tourism in some European Countries
by: Ioan Petroman, et al.
Published: (2023-11-01) -
POST COVID EVOLUTIONS OF TOURISM IN MEMBER STATES OF THE EUROPEAN UNION. SIMILARITIES AND DISPARITIES
by: ANIELA BĂLĂCESCU, et al.
Published: (2023-02-01) -
Tourism demand forecasting and tourists’ search behavior: evidence from segmented Baidu search volume
by: Yifan Yang, et al.
Published: (2021-12-01) -
The Strength and Dynamics of the Seasonal Concentration in Montenegrin Tourism
by: Miloš Bigović
Published: (2012-07-01) -
The impact of the economic policy uncertainty and geopolitical risks on tourism demand of Mexico
by: Veli YILANCI, et al.
Published: (2023-09-01)