TY - GEN
T1 - Cloud-Based Real-Time Tourism Demand Forecasting System with Deep Learning
AU - Zhang, Xinyan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Tourism industry plays an important role in global and regional economic growth. The precise and effective tourism demand forecast will serve as a crucial decision-making support for developing a sustainable and smart tourism ecosystem. Embracing the opportunities brought by the availability of high frequency internet big data and the development of deep learning-based forecasting models, this study proposes a cloud-based tourism demand forecasting system to provide real-time tourism demand forecasts and to promote industry collaboration.
AB - Tourism industry plays an important role in global and regional economic growth. The precise and effective tourism demand forecast will serve as a crucial decision-making support for developing a sustainable and smart tourism ecosystem. Embracing the opportunities brought by the availability of high frequency internet big data and the development of deep learning-based forecasting models, this study proposes a cloud-based tourism demand forecasting system to provide real-time tourism demand forecasts and to promote industry collaboration.
UR - https://www.scopus.com/pages/publications/85174409290
UR - https://www.mendeley.com/catalogue/64b80e79-2874-370a-b4fa-acdbc28fe3c8/
U2 - 10.1109/CASE56687.2023.10260628
DO - 10.1109/CASE56687.2023.10260628
M3 - Conference contribution
AN - SCOPUS:85174409290
T3 - IEEE International Conference on Automation Science and Engineering
BT - 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023
PB - IEEE Computer Society
T2 - 19th IEEE International Conference on Automation Science and Engineering, CASE 2023
Y2 - 26 August 2023 through 30 August 2023
ER -
