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Forecasting tourist arrivals at attractions: Search engine empowered methodologies

Volchek, Katerina, Liu, Anyu, Song, Haiyan and Buhalis, Dimitrios (2018) Forecasting tourist arrivals at attractions: Search engine empowered methodologies Tourism Economics.

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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 by predicting the number of visits to London museums. The number of visits to London museums is forecasted and the predictive powers of Naïve I, seasonal Naïve, SARMA, SARMAX, SARMAX-MIDAS 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 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 generation of weekly predictions, which are essential for attraction- and destination-level planning.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > School of Hospitality and Tourism Management
Authors :
Volchek, Katerina
Song, Haiyan
Buhalis, Dimitrios
Date : 19 November 2018
DOI : 10.1177/1354816618811558
Copyright Disclaimer : Copyright 2018 SAGE Publications
Uncontrolled Keywords : Forecasting, Google Trends, search engine, tourist demand, attractions, artificial intelligence
Depositing User : Melanie Hughes
Date Deposited : 28 Nov 2018 12:53
Last Modified : 28 Nov 2018 14:13

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