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A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model

Author

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  • Geun-Cheol Lee

    (College of Business, Konkuk University, Seoul 05029, Republic of Korea)

Abstract

Tourism is a core sector of Singapore’s economy, contributing significantly to Gross Domestic Product (GDP) and employment. Accurate tourism demand forecasting is essential for strategic planning, resource allocation, and economic stability, particularly in the post-COVID-19 era. This study develops a SARIMAX-based forecasting model to predict monthly visitor arrivals to Singapore, integrating web search data from Google Trends and external factors. To enhance model accuracy, a systematic selection process was applied to identify the effective subset of external variables. Results of the empirical experiments demonstrate that the proposed SARIMAX model outperforms traditional univariate models, including SARIMA, Holt–Winters, and Prophet, as well as machine learning-based approaches such as Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs). When forecasting the 24-month period of 2023 and 2024, the proposed model achieves the lowest Mean Absolute Percentage Error (MAPE) of 7.32%.

Suggested Citation

  • Geun-Cheol Lee, 2025. "A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model," Data, MDPI, vol. 10(5), pages 1-13, May.
    • Handle: RePEc:gam:jdataj:v:10:y:2025:i:5:p:73-:d:1653164
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    References listed on IDEAS

    as
    1. Haiyan Song & Zixuan Gao & Xinyan Zhang & Shanshan Lin, 2012. "A web-based Hong Kong tourism demand forecasting system," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 10(3/4), pages 275-291.
    2. Qiu, Richard T.R. & Wu, Doris Chenguang & Dropsy, Vincent & Petit, Sylvain & Pratt, Stephen & Ohe, Yasuo, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Asia and Pacific team," Annals of Tourism Research, Elsevier, vol. 88(C).
    3. David W. H. Wong & Andy C. L. Tai & David Y. T. Chan & Harry F. Lee, 2024. "Can tourism development and economic growth mutually reinforce in small countries? Evidence from Singapore," Current Issues in Tourism, Taylor & Francis Journals, vol. 27(8), pages 1316-1331, April.
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    Cited by:

    1. Ivanka Vasenska, 2025. "Comparative Analysis of Machine Learning and Deep Learning Models for Tourism Demand Forecasting with Economic Indicators," FinTech, MDPI, vol. 4(3), pages 1-22, September.

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