Journal
ANNALS OF TOURISM RESEARCH
Volume 83, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.annals.2020.102923
Keywords
Tourism volume forecasting; Long short-term memory networks; Search engine data; Weather data; Multivariate time series forecasting
Categories
Funding
- Humanities and Social Science Fund of Ministry of Education of China [20YJC630002]
- China Postdoctoral Science Foundation [2019M661000]
- National Natural Science Foundation of China [71971124, 71771043, 71932005]
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A novel approach based on long short-term memory (LSTM) networks that can incorporate multivariate time series data, including historical tourism volume data, search engine data and weather data, is proposed for forecasting the daily tourism volume of tourist attractions. The proposed approach is applied to forecast the daily tourism volume of Jiuzhaigou and Huangshan Mountain Area, two famous tourist attractions in China. Through these two applications, the validity of the proposed approach is verified. In addition, the forecasting power of the approach with historical data, search engine data and weather data is stronger than that without search engine data or without both search engine data and weather data, which provides evidence that search engine data and weather data are of great significance to tourism volume forecasting.
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