Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods
Hemantha Premakumara Diunugala (ORCiD), Beijing Institute of Technology, China, & Claudel Mombeuil (ORCiD), Beijing Institute of Technology, China
Published online: 30 October 2020, JTHSM, 6(3), pp.3-13.
Dublin Core Export
<?xml version='1.0' encoding='utf-8'?> <oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"> <dc:creator>Diunugala , Hemantha Premakumara</dc:creator> <dc:creator>Mombeuil, Claudel</dc:creator> <dc:date>2020-10-30</dc:date> <dc:description>Abstract: Purpose: This study compares three different methods to predict foreign tourist arrivals (FTAs) to Sri Lanka from top-ten countries and also attempts to find the best-fitted forecasting model for each country using five model performance evaluation criteria. Methods: This study employs two different univariate-time-series approaches and one Artificial Intelligence (AI) approach to develop models that best explain the tourist arrivals to Sri Lanka from the top-ten tourist generating countries. The univariate-time series approach contains two main types of statistical models, namely Deterministic Models and Stochastic Models. Results: The results show that Winter’s exponential smoothing and ARIMA are the best methods to forecast tourist arrivals to Sri Lanka. Furthermore, the results show that the accuracy of the best forecasting model based on MAPE criteria for the models of India, China, Germany, Russia, and Australia fall between 5 to 9 percent, whereas the accuracy levels of models for the UK, France, USA, Japan, and the Maldives fall between 10 to 15 percent. Implications: The overall results of this study provide valuable insights into tourism management and policy development for Sri Lanka. Successful forecasting of FTAs for each market source provide a practical planning tool to destination decision-makers.</dc:description> <dc:description>SUBMITTED: DEC 2019, REVISION SUBMITTED: APR 2020, 2nd REVISION SUBMITTED: MAY 2020, ACCEPTED: JUL 2020, REFEREED ANONYMOUSLY, PUBLISHED ONLINE: 30 OCT 2020</dc:description> <dc:identifier>https://zenodo.org/record/4055960</dc:identifier> <dc:identifier>10.5281/zenodo.4055960</dc:identifier> <dc:identifier>oai:zenodo.org:4055960</dc:identifier> <dc:language>eng</dc:language> <dc:relation>issn:2529-1947</dc:relation> <dc:relation>doi:10.5281/zenodo.4055959</dc:relation> <dc:rights>info:eu-repo/semantics/openAccess</dc:rights> <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights> <dc:source>Journal of Tourism, Heritage & Services Marketing 6(3) 3-13</dc:source> <dc:subject>foreign tourist arrivals</dc:subject> <dc:subject>winter's exponential smoothing</dc:subject> <dc:subject>ARIMA</dc:subject> <dc:subject>simple recurrent neural network</dc:subject> <dc:subject>Sri Lanka</dc:subject> <dc:title>Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods</dc:title> <dc:type>info:eu-repo/semantics/article</dc:type> <dc:type>publication-article</dc:type> </oai_dc:dc>