Forecasting British Tourist Arrivals to Balearic Islands Using Meteorological Variables and Artificial Neural Networks
AbstractThere is a clear understanding of the benefits of getting accurate predictions that allow diminishing the uncertainty inherent to the tourism activity. Managers, entrepreneurs, politicians and many other agents related to the tourism sector need good forecasts to plan an efficient use of tourism-related resources. In spite of the consensus on this need, tourism forecasters must make an even greater effort to satisfy the industry requirements. In this paper, the possibility of improving the predictive ability of a tourism demand model with meteorological explanatory variables is investigated using the case study of monthly British tourism demand to the Balearic Islands (Spain). For this purpose, a transfer function model and a causal artificial neural network are fitted. Meanwhile, the results are compared with those obtained by non-causal methods: an ARIMA model and an autoregressive neural network. The results seem to indicate that adding meteorological variables can increase the predictive power but, however, the most accurate prediction is obtained using a non-causal model, specifically an autoregressive neural network.
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Bibliographic InfoPaper provided by Centre de Recerca Econòmica (UIB ·"Sa Nostra") in its series CRE Working Papers (Documents de treball del CRE) with number 2008/2.
Length: 16 pages
Date of creation: 2008
Date of revision:
Publication status: Published in 'Documents de Treball CRE', 2008, pages 1-16
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Web page: http://www.cre.sanostra.es
Tourism; weather anomalies; climate change; transfer function modeling; United Kingdom.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-01-03 (All new papers)
- NEP-CMP-2009-01-03 (Computational Economics)
- NEP-ENV-2009-01-03 (Environmental Economics)
- NEP-FOR-2009-01-03 (Forecasting)
- NEP-TUR-2009-01-03 (Tourism Economics)
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