Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/27798
Title: Tourism forecasting and its relationship with leading economic indicators
Authors: Cho, V 
Keywords: Tourist forecast
Time series
ARIMA
Issue Date: 2001
Publisher: SAGE Publications
Source: Journal of hospitality and tourism research, 2001, v. 25, no. 4, p. 399-420 How to cite?
Journal: Journal of hospitality and tourism research 
Abstract: This article investigates the application of three time-series forecasting techniques, namely, exponential smoothing, univariate Autoregressive Integrated Moving Average (ARIMA), and adjusted ARIMA to predict travel demand (i.e., the number of arrivals) from different countries to Hong Kong. The third approach, adjusted ARIMA, is an enhancement of univariate ARIMA. This uses influential economic indicators that are highly correlated with travel demand to adjust univariate ARIMA. According to the analysis, adjusted ARIMA with economic indicators seems to be the best forecasting method for Japan, whereas univariate ARIMA is the best predictor for the United States and United Kingdom. Univariate ARIMA and adjusted ARIMA behave similarly for countries like Taiwan, Singapore, and Korea. Among the three forecasting methods, exponential smoothing is the least accurate. This shows that univariate ARIMA and adjusted ARIMA are more suitable and can be applied to forecast the fluctuating series of visitor arrivals.
URI: http://hdl.handle.net/10397/27798
ISSN: 1096-3480
EISSN: 1557-7554
DOI: 10.1177/109634800102500404
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