Forecasting Indonesia tourist arrivals to Malaysia based on nonlinear and linear model

Abstract

The development of economic and industry tourism depend upon how well the accuracy of number tourist arrivals forecasting is managed. The study aims to reduce computation complexity and enhance forecasting accuracy of decomposition ensemble model and wavelet method by incorporating intrinsic mode functions (IMFs) reconstruction. The empirical results indicated that the proposed model statistically outperformed all the considered benchmark models including the most popular wavelet with support vector machine (WSVM) model, decomposition ensemble model (Benchmark EMD-SARIMA and EMD-WSVM). To determine the performance, four statistical measures were applied, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). Then, the best ranked model is measured using Mean of Forecasting Error (MFE) to determine its under and over-predicted forecast rate. The results show that EMD-WSVM ranked first based on four measures for Thailand tourist arrivals. The MFE results also indicates a small value of over-predicted values compared to the observed tourist arrivals values for Indonesia. The MAPE of the proposed EMD-WSVM data of Indonesia is <10% that indicate as excellent fit. In conclusion, the proposed method of pre-processing data using EMD and wavelet method enhanced the forecasting accuracy of the SVM model.