Abstract
Quantitative methods to forecasting tourist arrivals can be sub-divided into causal methods
and non-causal methods. Non-causal time series methods remain popular tourism forecasting
tools due to the accuracy of their forecasting ability and general ease of use. Since tourist
arrivals exhibit seasonality, SARIMA models are often found to be the most accurate.
However, these models assume that the time-series is linear. This paper compares the
baseline seasonal Naïve and SARIMA forecasts of a seasonal tourist destination faced with a
structural break in the data, with alternative non-linear methods, with the aim to determine
the accuracy of the various methods. These methods include the unobserved components
model, smooth transition autoregressive model (STAR) and singular spectrum analysis
(SSA). The results show that the non-linear forecasts outperform the other methods. The
linear methods show some superiority in short-term forecasts when there are no structural
changes in the time series.