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

Long Wen, Chang Liu ^[1] ; Haiyan Song ^[2] ; Long Wen ^[3]
1. [1] University of Nottingham Ningbo China
  
  University of Nottingham Ningbo China
  
  China
2. [2] Hong Kong Polytechnic University
  
  Hong Kong Polytechnic University
  
  RAE de Hong Kong (China)
3. [3] University of Nottingham
  
  University of Nottingham
  
  Reino Unido
Mostrar afiliaciones +
Localización: Tourism economics: the business and finance of tourism and recreation, ISSN 1354-8166, Vol. 25, Nº. Extra 3, 2019 (Ejemplar dedicado a: Tourism forecasting – New trends and issues), págs. 309-329
Idioma: inglés
Texto completo no disponible (Saber más ...)

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