A novel deep learning approach for tourism volume forecasting with tourist search data
Bibliographic Data
| ID | 21653862 |
|---|---|
| Authors | Mingchen Li (0000-0002-0736-1541, Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China), Chengyuan Zhang (0000-0001-8704-8856, School of Economics and Management Xidian University Xi'an China), Shaolong Sun (0000-0002-3196-1459, School of Management Xi'an Jiaotong University Xi'an China, corresponding author), Shouyang Wang (0000-0001-5773-998X, Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China) |
| Year | 2023 |
| Volume | 25 |
| Issue | 2 |
| Pages | 183-197 |
| Publication date | 2023-03-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Tourism Research (JOURNAL) |
| Journal identifiers | ISSN: 1099-2340 • E-ISSN: 1522-1970 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1002/jtr.2558 |
| OpenAlex | W4308454157 |
| Language | EN |
| Citations received | 9 |
| References cited | 57 |
Tourism volume forecasting is the hot topic in tourism management, and deep learning techniques as the promising tool are becoming popular for capturing the characteristics of tourism volume data, which is reflected in two aspects: data dimension reduction (i.e., stacked autoencoders [SAE]) and model forecasting (i.e., bi‐directional gated recurrent unit neural network [Bi‐GRU]). With Hong Kong inbound arrivals as a case, this study has empirically verified that deep learning techniques can improve forecasting accuracy. Furthermore, the proposed approach (i.e., SAE‐Bi‐GRU) is significantly superior to benchmark models (i.e., PCA‐Bi‐GRU with Baidu index and Google trends)
Artificial neural network · Cartography · Data mining · Data science · Deep learning · Dimensionality reduction · Geography · Machine learning · Tourism · World Wide Web · Computer Science · Diverse Aspects of Tourism Research · Mathematics · Sport and Mega-Event Impacts · Wine Industry and Tourism · Artificial Intelligence
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| Citations per year | 3 |
|---|---|
| Citation span | 2023 - 2026 (4) |
| Highly cited | No |