Machine Learning in Internet Search Query Selection for Tourism Forecasting

Prior studies have shown that Internet search query data have great potential to improve tourism forecasting. As such, selecting the most relevant information from large amounts of search query data is crucial to enhancing forecasting accuracy and reducing overfitting; however, such feature selection methods have not been considered in the tourism forecasting literature. This study employs four machine learning–based feature selection methods to extract useful search query data and construct relevant econometric models. We examined the proposed methods based on monthly forecasting of tourist arrivals in Beijing, China, along with weekly forecasting of hotel occupancy in the city of Charleston, South Carolina, USA. Our findings indicate that the forecasting model with the selected search keywords outperformed the benchmark ARMAX model without feature selection in forecasting tourism demand and hotel occupancy. Therefore, machine learning methods can identify the most useful search query data to significantly improve forecasting accuracy in tourism and hospitality.



Work Title Machine Learning in Internet Search Query Selection for Tourism Forecasting
Open Access
  1. Xin Li
  2. Hengyun Li
  3. Bing Pan
  4. Rob Law
License In Copyright (Rights Reserved)
Work Type Article
  1. Journal of Travel Research
Publication Date July 1, 2021
Publisher Identifier (DOI)
Deposited July 21, 2022




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Work History

Version 1

  • Created
  • Added ForecastingTouristDemandWithSearchIndexTourismManagement__1_.pdf
  • Added Creator Xin Li
  • Added Creator Hengyun Li
  • Added Creator Bing Pan
  • Added Creator Rob Law
  • Published
  • Updated

Version 2

  • Created
  • Deleted ForecastingTouristDemandWithSearchIndexTourismManagement__1_.pdf
  • Added Machine learning in internet search query selection for tourism forecasting.pdf
  • Published
  • Updated Keyword, Publication Date Show Changes
    • Tourism forecasting, Hotel occupancy, Search query data, Machine learning, Feature selection
    Publication Date
    • 2021-07-01
    • 2020-07-05
  • Updated