An Interval Decomposition-Ensemble Model for Tourism Forecasting

Datos Bibliográficos

ID7159684
AutoresGang Xie (0000-0003-1421-4838, Beijing Technology and Business University, autor de correspondencia), Shuihan Liu (0000-0001-8622-5865, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China), Xin Li (0000-0002-3425-8566, University of Science and Technology Beijing)
Año2025
Volumen49
Número3
Páginas600-614
Fecha de publicación2025-03-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of Hospitality & Tourism Research (JOURNAL)
Identificadores de la revistaISSN: 1096-3480 • E-ISSN: 1557-7554
EditorialSAGE Publications Inc (PUBLISHER)
DOI10.1177/10963480231198539
OpenAlexW4386845977
IdiomaEN
Citas recibidas1
Referencias citadas50

In order to accurately capture the variability of tourism demand, this paper proposed a new decomposition-ensemble framework for forecasting interval-valued time series (ITS) of tourism arrivals. The procedure consists of four main steps: ITS decomposition, determination of the optimal decomposition technique, component ITS forecasting, and ensemble. The investigation revealed the optimal theoretical approach for choosing the decomposition technique in terms of multi-scale complexity. In addition, a comparison was made between the performance of two types of models that predict the upper and lower limits of ITS separately versus simultaneously. Using the weekly ITSs of tourist arrivals to Mount Siguniang, in western China, and Hawaii, USA, during both COVID and non-COVID periods, an empirical study was conducted to illustrate the framework. The results demonstrated that the proposed model exhibits higher predictive accuracy and greater robustness, compared to other models. This indicates the model’s effectiveness in forecasting the ITS of tourism demand

Decomposition · Econometrics · Ensemble forecasting · Geography · Tourism · Computer Science · Energy Load and Power Forecasting · Forecasting Techniques and Applications · Mathematics · Stock Market Forecasting Methods · Artificial Intelligence