Analyzing and forecasting tourism demand with ARAR algorithm

This study investigates the ARAR model and its usefulness as a forecast-generating mechanism for tourism demand for nine major tourist destinations in the Asian-Pacific region. The analysis is conducted on monthly inflow of international visitors covering the period 1975:01–2006:12. In monthly series, we consider 6-, 12-, 18-, and 24-months-ahead forecasting horizons. The accuracy of the forecasts in most cases is robust to the forecasting horizon based on such forecasting performance metric as mean absolute percentage error (MAPE) and root mean square error (RMSE). The study is further expanded by including quarterly series in the forecasting exercise to ensure the reliability of the forecasting evaluation. The 2-, 4-, 6-, and 8-quarters-ahead forecasting horizons are investigated. A comparison between forecasts generated by monthly and quarterly data reveals that the performance is broadly similar. Finally, the forecasts of ARAR are compared with those of seasonal ARIMA, which has been proven reliable in many forecasting contexts. Indeed, ARAR model can be deemed as credible alternatives when modeling and forecasting tourism demand.