Common trends in international tourism demand: Are they useful to forecast tourism predictions?
This study evaluates whether modelling the existing commont trends in tourism arrivals from all visitor markets to a specific destination can improve tourism predictions. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial N...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/66855 |
| Acceso en línea: | https://hdl.handle.net/2445/66855 |
| Access Level: | acceso abierto |
| Palabra clave: | Previsió econòmica Turisme Desenvolupament econòmic Xarxes neuronals (Informàtica) Economic forecasting Tourism Economic development Neural networks (Computer science) |
| Sumario: | This study evaluates whether modelling the existing commont trends in tourism arrivals from all visitor markets to a specific destination can improve tourism predictions. While most tourism forecasting research focuses on univariate methods, we compare the performance of three different Artificial Neural Networks in a multivariate setting that takes into account the correlations in the evolution of inbound international tourism demand to Catalonia (Spain). We find that the multivariate multiple-output approach does not outperform the forecasting performance of the networks when applied country by country, but it significantly outperforms the forecasting performance for total tourist arrivals. |
|---|