Forecasting ASEAN Tourist Arrivals in Malaysia using Different Time Series Models
A. Rafidah1, Ernie Mazuin2, Ani Shabri3
1A. Rafidah, Lecturer, Department of Technical Foundation, Universiti Kuala Lumpur, Bandar Seri Alam, johor Bahru Malaysia.
2Ernie Mazuin, Lecturer, Department of Instrumentation and Control Engineering, Universiti Kuala Lumpur, Bandar Seri Alam, Johor Bahru Malaysia.
3Ani Shabri, Associate Professor, Department of Mathematics, Universiti Technology Malaysia, Skudai Johor Bahru, Malaysia.
Manuscript received on 27 September 2019 | Revised Manuscript received on 09 November 2019 | Manuscript Published on 22 November 2019 | PP: 572-578 | Volume-8 Issue-6S3 September 2019 | Retrieval Number: F11010986S319/19©BEIESP | DOI: 10.35940/ijeat.F1101.0986S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this study three time series models are used for forecasting monthly ASEAN tourist arrivals in Malaysia from January 1999 to December 2015. Brunei, Thailand and Vietnam of ASEAN country selected as case study. This paper compares the forecasting accuracy of seasonal autoregressive integrated moving average (SARIMA), Support Vector Machine (SVM) and Wavelet Support Vector Machine (WSVM) and Empirical Mode Decomposition with Wavelet Support Vector Machine (EMD_WSVM) using root mean square error (RMSE) and mean absolute percentage error (MAPE) criterion. Moreover, correlation test has also been carried out to strengthen decisions, and to check accuracy of various forecasting models. Based on the forecasting performance of all four models, hybrid model SARIMA and EMD_WSVM are found to be best models as compare to single model SVM and hybrid model WSVM.
Keywords: Forecasting, Tourist Arrivals, SARIMA Model, SVM Model, WSVM Model and EMD_WSVM Model.
Scope of the Article: Probabilistic Models and Methods