Simple v/s Sophisticated Methods of Forecasting for Mauritius Monthly Tourist Arrival Data

Abstract

Making proper forecast of the number of tourist arrivals in Mauritius is essential as tourism is a pillar of the Mauritian economy. This paper compares the in-sample and out-of-sample forecast accuracy for the additive version of the Holt-Winters (HW) method, the Autoregressive Integrated Moving Average (ARIMA) method and the Neural Network Autoregression (NNAR) method applied on the monthly tourist arrival data for Mauritius. The aim is to demonstrate that a simple method of forecasting can be more accurate than a complex one in forecasting monthly tourist arrival. To measure the forecast accuracy of the three models, the Mean Absolute Percentage error (MAPE) and the Mean Absolute Scale Error (MASE) are considered as they are scale independent. The results obtained from the MAPE and MASE show that the NNAR model considered as a sophisticated model is poorer than the HW and ARIMA method over both the in-sample and out-of-sample data.