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Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods

[journal article]

Diunugala, Hemantha Premakumara
Mombeuil, Claudel

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 differen... view more

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.... view less

Keywords
Sri Lanka; tourism; trend; prognosis; model

Classification
Leisure Research
Economic Sectors

Free Keywords
foreign tourist arrivals; winter's exponential smoothing; ARIMA; simple recurrent neural network

Document language
English

Publication Year
2020

Page/Pages
p. 3-13

Journal
Journal of Tourism, Heritage & Services Marketing, 6 (2020) 3

DOI
https://doi.org/10.5281/zenodo.4055960

ISSN
2529-1947

Status
Published Version; peer reviewed

Licence
Creative Commons - Attribution-Noncommercial-No Derivative Works 4.0


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© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.