This study focuses firstly on the importance for forecasting accuracy of allowing for intervention events in the modeling process. Seasonal autoregressive integrated moving average (SARIMA) models are therefore estimated both with and without intervention effects (the September 11, 2001 events) using data for the period 1990–2001. These models are used to generate forecasts for 2002 and the first part of 2003, and forecast accuracy is assessed using mean absolute percentage error and root mean square percentage error. The second focus of the study is to examine the impacts on tourism demand of the major crises that occurred during the period 2001–2003. The chosen US metropolitan destination is New York City, which was severely affected by the September 11 events, and within New York the US Metropolitan Museum of Art is selected, as this is a very well-known and visited destination for which seasonal data are available over the period 1990–2003. The artificial neural networks (ANNs) and SARIMA forecasts are compared with forecasts generated by the much simpler automatic Holt-Winter's seasonal double exponential smoothing model as well as two naïve forecasting models to ensure that minimum performance standards are being met.
Chen, R. (2007), "Impacts of an Intervention Event on Museum Visitations", Chen, J. (Ed.) Advances in Hospitality and Leisure (Advances in Hospitality and Leisure, Vol. 3), Emerald Group Publishing Limited, Bingley, pp. 55-68. https://doi.org/10.1016/S1745-3542(06)03004-9Download as .RIS
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