New Paper Published – Volchek, K., Liu, A., Song, H., Buhalis, D. (2018). Forecasting tourist arrivals at attractions: Search engine empowered methodologies. Tourism Economics. https://doi.org/10.1177/1354816618811558
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
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We have been informed by MRC of changes to the way that NHS research costs will be attributed and funded. You may already be aware but the Department of Health and NIHR have introduced a new attribution tool called the Schedule of Events Attribution Tool (SoECAT) which will now be mandatory for any researcher wishing to access resources within the NHS for their study. The Pilot stage for the project began on 1st October 2018 and there are three main changes;
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