Forecasting tourist arrivals at attractions: Search engine empowered methodologies

Katerina Volchek, Anyu Liu, Haiyan Song and Dimitrios Buhalis
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Katerina Volchek: The Hong Kong Polytechnic University, Hong Kong
Anyu Liu: University of Surrey, UK
Haiyan Song: The Hong Kong Polytechnic University, Hong Kong
Dimitrios Buhalis: Bournemouth University, UK

Tourism Economics, 2019, vol. 25, issue 3, 425-447

Abstract: Tourist decision to visit attractions is a complex process influenced by multiple factors of individual context. This study investigates how the accuracy of tourism demand forecasting can be improved at the micro level. The number of visits to five London museums is forecast and the predictive powers of Naïve I, seasonal Naïve, seasonal autoregressive moving average, seasonal autoregressive moving average with explanatory variables, SARMAX-mixed frequency data sampling and artificial neural network models are compared. The empirical findings extend understanding of different types of data and forecasting algorithms to the level of specific attractions. Introducing the Google Trends index on pure time-series models enhances the forecasts of the volume of arrivals to attractions. However, none of the applied models outperforms the others in all situations. Different models’ forecasting accuracy varies for short- and long-term demand predictions. The application of higher frequency search query data allows for the generation of weekly predictions, which are essential for attraction- and destination-level planning.

Keywords: artificial intelligence; attractions; forecasting; Google Trends; search engine; tourist demand (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (20)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:25:y:2019:i:3:p:425-447

DOI: 10.1177/1354816618811558

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