An Interval Decomposition-Ensemble Model for Tourism Forecasting
Bibliographic Data
| ID | 7159684 |
|---|---|
| Authors | Gang Xie (0000-0003-1421-4838, Beijing Technology and Business University, corresponding author), 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) |
| Year | 2025 |
| Volume | 49 |
| Issue | 3 |
| Pages | 600-614 |
| Publication date | 2025-03-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Hospitality & Tourism Research (JOURNAL) |
| Journal identifiers | ISSN: 1096-3480 • E-ISSN: 1557-7554 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/10963480231198539 |
| OpenAlex | W4386845977 |
| Language | EN |
| Citations received | 1 |
| References cited | 50 |
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
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| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |