This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Cho, V. Search for Related Content Social Bookmarking                   What's this?

Tourism Forecasting and its Relationship with Leading Economic Indicators

The Hong Kong Polytechnic University, mvscho{at}polyu.edu.hk

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.

Key Words: tourist forecast • time series • ARIMA

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?