The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Tourism demand forecasting: A deep learning approach
Open accessAbstract
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.
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A fast learning algorithm for deep belief nets
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Detecting Novel Associations in Large Data Sets
Reshef, D. N., Reshef, Y. A., Finucane, H. K., Grossman, S. R., McVean, G., Turnbaugh, P. J., Lander, E. S., Mitzenmacher, M., & Sabeti, P. C. (2011) · Science · doi:10.1126/science.1205438 · Cited by 3,339
Cited by
An optimized model using LSTM network for demand forecasting
Abbasimehr, H., Shabani, M., & Yousefi, M. (2020) · Computers & Industrial Engineering · doi:10.1016/j.cie.2020.106435 · Cited by 538
Metadata
- Authors
- Law, R., Li, G., Fong, D. K. C., & Han, X.
- Venue
- Annals of Tourism Research
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 75
- Pages
- 410-423
- ISSN
- 0160-7383 (ISSN-L)
- OpenAlex
- W2939094371
- Cited by
- 448
- References
- 53
- FWCI
- 35.77
- Type
- journal article
- Language
- en
- Open access
- green
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- Law, R., Li, G., Fong, D. K. C., & Han, X. (2019). Tourism demand forecasting: A deep learning approach. Annals of Tourism Research. https://doi.org/10.1016/j.annals.2019.01.014
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