Advancing tourism demand forecasting in Sri Lanka

Evaluating the performance of machine learning models and the impact of social media data integration

Dados Bibliográficos

ID19554194
AutoresIsuru Udayangani Hewapathirana (0000-0003-4843-0993, University of Kelaniya, autor correspondente)
Ano2025
Volume11
Fascículo2
Páginas261-285
Data de publicação2025-05-15
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoJournal of Tourism Futures (JOURNAL)
Identificadores do periódicoISSN: 2055-5911 • E-ISSN: 2055-592X
EditoraEmerald (PUBLISHER)
DOI10.1108/jtf-06-2023-0149
OpenAlexW4389682937
IdiomaEN
Citações recebidas4
Referências citadas40

Purpose This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka. Design/methodology/approach Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated. Findings The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends. Practical implications The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector. Originality/value This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain

Artificial neural network · Autoregressive integrated moving average · Autoregressive model · Data mining · Data science · Econometrics · Geography · Machine learning · Originality · Social media · Social science · Sociology · Sri lanka · Support vector machine · Time series · Tourism · World Wide Web · Computer Science · Digital Marketing and Social Media · Diverse Aspects of Tourism Research · Mathematics · Sport and Mega-Event Impacts · Artificial Intelligence