A comparison forecasting methods for trend and seasonal Indonesia tourist arrivals time series

Abstract

This study aimed to determine the accuracy of forecasting methods for trend and seasonal Indonesia visitor arrival. Three single methods, Seasonal Autoregressive Moving Average (SARIMA), Singular Spectrum Analysis (SSA), and Fuzzy Time Series (FTS) were used to model and predict the monthly arrival. We have also compared the three single methods with two hybrid approaches, SARIMA-FTS, and SSA-FTS. Results show that SARIMA-FTS is the most appropriate model to capture the trend and seasonal pattern of the series in term of Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).