A deep learning approach for daily tourist flow forecasting with consumer search data
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Tourism College of Beijing Union University, Beijing, People’s Republic of China
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Publication type: Journal Article
Publication date: 2020-01-05
scimago Q1
wos Q2
SJR: 0.885
CiteScore: 7.2
Impact factor: 3.3
ISSN: 10941665, 17416507
Geography, Planning and Development
Tourism, Leisure and Hospitality Management
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Metrics
59
Total citations:
59
Citations from 2024:
20
(33.89%)
Cite this
GOST |
RIS |
BibTex |
MLA
Cite this
GOST
Copy
Zhang B. et al. A deep learning approach for daily tourist flow forecasting with consumer search data // Asia Pacific Journal of Tourism Research. 2020. Vol. 25. No. 3. pp. 323-339.
GOST all authors (up to 50)
Copy
Zhang B., Li N., Shi F., Law R. A deep learning approach for daily tourist flow forecasting with consumer search data // Asia Pacific Journal of Tourism Research. 2020. Vol. 25. No. 3. pp. 323-339.
Cite this
RIS
Copy
TY - JOUR
DO - 10.1080/10941665.2019.1709876
UR - https://doi.org/10.1080/10941665.2019.1709876
TI - A deep learning approach for daily tourist flow forecasting with consumer search data
T2 - Asia Pacific Journal of Tourism Research
AU - Zhang, Binru
AU - Li, Nao
AU - Shi, Feng
AU - Law, Rob
PY - 2020
DA - 2020/01/05
PB - Taylor & Francis
SP - 323-339
IS - 3
VL - 25
SN - 1094-1665
SN - 1741-6507
ER -
Cite this
BibTex (up to 50 authors)
Copy
@article{2020_Zhang,
author = {Binru Zhang and Nao Li and Feng Shi and Rob Law},
title = {A deep learning approach for daily tourist flow forecasting with consumer search data},
journal = {Asia Pacific Journal of Tourism Research},
year = {2020},
volume = {25},
publisher = {Taylor & Francis},
month = {jan},
url = {https://doi.org/10.1080/10941665.2019.1709876},
number = {3},
pages = {323--339},
doi = {10.1080/10941665.2019.1709876}
}
Cite this
MLA
Copy
Zhang, Binru, et al. “A deep learning approach for daily tourist flow forecasting with consumer search data.” Asia Pacific Journal of Tourism Research, vol. 25, no. 3, Jan. 2020, pp. 323-339. https://doi.org/10.1080/10941665.2019.1709876.