| Status | 已發表Published |
| Tourism demand forecasting: A deep learning approach | |
Law, R. ; Li, G.; Fong, K. C.; Han, X.
| |
| 2019-01-29 | |
| Source Publication | Annals of Tourism Research
 |
| ISSN | 0160-7383 |
| Pages | 410-423 |
| Abstract | Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. Using a deep learning approach, this research studied the framework in forecasting monthly Macau tourist arrival volumes. The empirical results demonstrated that the deep learning approach significantly outperforms support vector regression and artificial neural network models. Moreover, the construction and identification of highly relevant features from the proposed deep network architecture provide practitioners with a means of understanding the relationships between various tourist demand forecasting factors and tourist arrival volumes. |
| Keyword | Tourism demand forecasting Deep learning Long-short-term-memory Attention mechanism Feature engineering Lag order |
| URL | View the original |
| Language | 英語English |
| The Source to Article | PB_Publication |
| PUB ID | 45093 |
| Document Type | Journal article |
| Collection | DEPARTMENT OF INTEGRATED RESORT AND TOURISM MANAGEMENT |
| Corresponding Author | Law, R. |
| Recommended Citation GB/T 7714 | Law, R.,Li, G.,Fong, K. C.,et al. Tourism demand forecasting: A deep learning approach[J]. Annals of Tourism Research, 2019, 410-423. |
| APA | Law, R.., Li, G.., Fong, K. C.., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research, 410-423. |
| MLA | Law, R.,et al."Tourism demand forecasting: A deep learning approach".Annals of Tourism Research (2019):410-423. |
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