Improving Accuracy of Tourist Demand Estimation of Asian Countries
محورهای موضوعی : Geography and tourism planning, geography and urban planning, urban planning, architecture, geography and rural planning, political geography
Arshin Bakhtiari
1
,
Yuhanis Abdul Aziz
2
,
Azmawani Abdul Rahman
3
,
Rosmah Mohamed
4
1 - School of Business and Economics, University Putra Malaysia, Selangor, Malaysia
2 - School of Business and Economics, Universiti Putra Malaysia, Selangor, Malaysia
3 - School of Business and Economics, Universiti Putra Malaysia, Selangor, Malaysia
4 - School of Business and Economics, Universiti Putra Malaysia, Selangor, Malaysia
کلید واژه: Asian Countries, ARIMA, Soft computing, South Korea, Tourism demand.,
چکیده مقاله :
Due to the importance of accurate tourism demand estimation, the evaluation of estimating approaches is still ongoing. To address this challenge, the current study aimed to present a novel estimation statistical approach for modifying ARIMA to compare with two most prominent soft computing approaches, ANN and SVM. ARIMAadj is the modified ARIMA seasonal adjustment that declares a potential replacement to conventional ARIMA. Current study investigated the accuracy of seasonal adjustment on conventional ARIMA and compared its accuracy with ANN and SVM in estimating tourist demand of Asian countries to South Korea. The results show that the modified ARIMA outperform the soft computing approaches for tourism demand estimation accuracy of five out of six source Asian countries. Therefore, it could be concluded although there is no optimal approach to estimate tourist arrivals with certainty, the findings of this study show that the seasonal adjustment in ARIMA would be a worthwhile model to estimate tourism demand of Asian countries.
Due to the importance of accurate tourism demand estimation, the evaluation of estimating approaches is still ongoing. To address this challenge, the current study aimed to present a novel estimation statistical approach for modifying ARIMA to compare with two most prominent soft computing approaches, ANN and SVM. ARIMAadj is the modified ARIMA seasonal adjustment that declares a potential replacement to conventional ARIMA. Current study investigated the accuracy of seasonal adjustment on conventional ARIMA and compared its accuracy with ANN and SVM in estimating tourist demand of Asian countries to South Korea. The results show that the modified ARIMA outperform the soft computing approaches for tourism demand estimation accuracy of five out of six source Asian countries. Therefore, it could be concluded although there is no optimal approach to estimate tourist arrivals with certainty, the findings of this study show that the seasonal adjustment in ARIMA would be a worthwhile model to estimate tourism demand of Asian countries.
