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Data source combination for tourism demand forecasting

Author

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  • Mingming Hu

    (12664Guangxi University; The Hong Kong Polytechnic University, China)

  • Haiyan Song

    (26680The Hong Kong Polytechnic University, China)

Abstract

Search engine data are of considerable interest to researchers for their utility in predicting human behaviour. Recently, search engine data have also been used to predict tourism demand (TD). Models developed based on such data generate more accurate forecasts of TD than pure time-series models. The aim of this article is to examine whether combining causal variables with search engine data can further improve the forecasting performance of search engine data models. Based on an artificial neural network framework, 168 observations during 2005–2018 for short-haul travel from Hong Kong to Macau are involved in the test, and the empirical results suggest that search engine data models with causal variables outperform models without causal variables and other benchmark models.

Suggested Citation

  • Mingming Hu & Haiyan Song, 2020. "Data source combination for tourism demand forecasting," Tourism Economics, , vol. 26(7), pages 1248-1265, November.
    • Handle: RePEc:sae:toueco:v:26:y:2020:i:7:p:1248-1265
    DOI: 10.1177/1354816619872592
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    References listed on IDEAS

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