Please use this identifier to cite or link to this item: http://ir.unikl.edu.my/jspui/handle/123456789/21133
metadata.conference.dc.title: FORECASTING ASEAN TOURIST ARRIVALS IN MALAYSIA USING DIFFERENT TIME SERIES MODELS
metadata.conference.dc.contributor.*: A. RAFIDAH
ANI SHABRI
(UniKL MITEC)
metadata.conference.dc.subject: Forecasting;
Tourist arrivals
SARIMA model
SVM model
WSVM model
EMD_WSVM model
metadata.conference.dc.date.issued: 18-Feb-2019
metadata.conference.dc.description.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.
metadata.conference.dc.identifier.uri: http://ir.unikl.edu.my/jspui/handle/123456789/21133
metadata.conference.dc.conference.name: International Conference on Research Approaches in Applied Sciences, Computer and Engineering Sciences, Industrial Technology & IT Applications (RACEI-FEB-2019)
metadata.conference.dc.conference.year: 2019
Appears in Collections:Conference Papers



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