Forecasting British Tourist Inflows to Portugal Using Google Trends Data

Gorete Dinis (), Carlos Costa () and Osvaldo Pacheco ()
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Gorete Dinis: Polytechnic Institute of Portalegre – Higher School of Education of Portalegre and Social Sciences – Praça da República
Carlos Costa: Management and Industrial Engineering, University of Aveiro, Campus Universitário de Santiago
Osvaldo Pacheco: University of Aveiro, Campus Universitário de Santiago

A chapter in Tourism, Culture and Heritage in a Smart Economy, 2017, pp 483-496 from Springer

Abstract: Abstract Purpose—The purpose of this paper is to explore the Google Trends (GT) data in order to understand the behavior and interests of British tourists in Portugal as a tourist destination and to verify if the GT data correlates with the tourism official data of Portugal. Furthermore, it will investigate if GT data can improve forecasts on the arrival of British tourists to Portugal. Design/methodology/approach—We used GT data on a set of search terms to predict the demand for hotel establishments by UK residents in Portugal and employed the Autoregressive Integrated Moving Average (ARIMA) model and Transfer Function (TF) to evaluate the usefulness of this data. Furthermore, we correlated the GT data with official tourism data of Portugal. Findings—The TF models outperformed their ARIMA counterparts, meaning that the TF models which considered the GT index produced more accurate forecasts. Practical implications—The paper contributes to increase the knowledge on the potential of Google-based search data in order to understand the behaviour patterns of predicted British travelers to Portugal and help to predict the British tourist inflows to Portugal. Originality/value—The paper is novel because it is the first in the field of hospitality and tourism to predict British tourists inflows to Portugal and it is a unique paper in this area that used several keywords in order to define a tourist destination.

Keywords: Tourism; Google trends; Forecasting; Portugal; Transfer Function (search for similar items in EconPapers)
JEL-codes: Z (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/978-3-319-47732-9_32

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