volume 25 issue 3 pages 323-339

A deep learning approach for daily tourist flow forecasting with consumer search data

Publication typeJournal Article
Publication date2020-01-05
scimago Q1
wos Q2
SJR0.885
CiteScore7.2
Impact factor3.3
ISSN10941665, 17416507
Geography, Planning and Development
Tourism, Leisure and Hospitality Management
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Cite this
GOST |
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.
RIS |
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 -
BibTex |
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}
}
MLA
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.