Forecasting tourism demand using search query data: A hybrid modelling approach

Long Wen, Chang Liu and Haiyan Song
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Chang Liu: University of Nottingham Ningbo China, People’s Republic of China
Haiyan Song: The Hong Kong Polytechnic University, Hong Kong

Tourism Economics, 2019, vol. 25, issue 3, 309-329

Abstract: Search query data have recently been used to forecast tourism demand. Linear models, particularly autoregressive integrated moving average with exogenous variable models, are often used to assess the predictive power of search query data. However, they are limited by their inability to model non-linearity due to their pre-assumed linear forms. Artificial neural network models could be used to model non-linearity, but mixed results indicate that their application is not appropriate in all situations. Therefore, this study proposes a new hybrid model that combines the linear and non-linear features of component models. The model outperforms other models when forecasting tourist arrivals in Hong Kong from mainland China, thus demonstrating the advantage of adopting hybrid models in forecasting tourism demand with search query data.

Keywords: artificial neural network; hybrid specification; non-linear model; search query data; tourism forecasting (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:toueco:v:25:y:2019:i:3:p:309-329

DOI: 10.1177/1354816618768317

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