A comparison of three different approaches to tourist arrival forecasting

Forecasting plays a major role in tourism planning. The promotion of tourism projects involving substantial sums of money requires an estimate of future demand and market penetration. The commitment to developing tourism would be much easier if it were possible to analyse current and past tourist traffic and predict the nature of changes in tourism demand. These extrapolative approaches to forecasting require historical data. This paper investigates the application of three time-series forecasting techniques, namely exponetial smoothing, univariate ARIMA, and Elman's Model of Artificial Neural Networks (ANN), to predict travel demand (i.e. the number of arrivals) from different countries to Hong Kong. Exponential smoothing and ARIMA are two commonly used statistical time series forecasting techniques. The third approach, Neural Networks, is an artificial intelligence technique derived from computer science. According to the analysis presented in this paper, Neural Networks seems to be the best method for forecasting visitor arrivals, especially those series without obvious pattern.