Modeling and forecasting tourism demand for arrivals with stochastic nonstationary seasonality and intervention

This paper presents the use of time series SARIMA and MARIMA with interventions in forecasting tourism demand using ten arrival series for Hong Kong. Augmented Dickey-Fuller tests indicated that all the series were seasonal nonstationary. Significant interventions such as relaxation of the issuance of out-bound visitors visas, the Asian financial crisis, the handover, and the bird flu epidemic were all empirically identified with significant test results and expected signs. The forecasts obtained using models that capture stochastic nonstationary seasonality and interventions, SARIMA and MARIMA with intervention analysis, are compared with other eight time series models and were found to have the highest accuracy.