{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T01:01:22Z","timestamp":1781226082109,"version":"3.54.1"},"reference-count":21,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T00:00:00Z","timestamp":1746835200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Tourism is a core sector of Singapore\u2019s 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\u2013Winters, 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%.<\/jats:p>","DOI":"10.3390\/data10050073","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T03:59:26Z","timestamp":1747108766000},"page":"73","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8555-7064","authenticated-orcid":false,"given":"Geun-Cheol","family":"Lee","sequence":"first","affiliation":[{"name":"College of Business, Konkuk University, Seoul 05029, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ngoc, B.H., Hoang, C.C., and Tram, N.H.M. (2024). A Time-Varying Analysis between Economic Uncertainty and Tourism Development in Singapore. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0302980"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1080\/13683500.2023.2213879","article-title":"Can Tourism Development and Economic Growth Mutually Reinforce in Small Countries? Evidence from Singapore","volume":"27","author":"Wong","year":"2024","journal-title":"Curr. Issues Tour."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Frechtling, D. (2012). Forecasting Tourism Demand, Routledge.","DOI":"10.4324\/9780080494968"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1177\/004728759303200209","article-title":"Forecasting Tourism: A Sine Wave Time Series Regression Approach","volume":"32","author":"Chan","year":"1993","journal-title":"J. Travel Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1177\/004728759803600309","article-title":"Forecasting Tourist Arrivals: Nonlinear Sine Wave or ARIMA?","volume":"36","author":"Chu","year":"1998","journal-title":"J. Travel Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1177\/004728759903700409","article-title":"Modeling the Impact of Sudden Environmental Changes on Visitor Arrival Forecasts: The Case of the Gulf War","volume":"37","author":"Chan","year":"1999","journal-title":"J. Travel Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1016\/j.tourman.2008.10.016","article-title":"Forecasting Tourism Demand with ARMA-Based Methods","volume":"30","author":"Chu","year":"2009","journal-title":"Tour. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"107","DOI":"10.18089\/tms.2016.12111","article-title":"Forecasting Tourist In-Flow in South East Asia: A Case of Singapore","volume":"12","author":"Kumar","year":"2016","journal-title":"Tour. Manag. Stud."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.econmod.2016.09.001","article-title":"Effective Timing of Tourism Policy: The Case of Singapore","volume":"60","author":"Agiomirgianakis","year":"2017","journal-title":"Econ. Model."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1142\/S0219622022500365","article-title":"Forecasting Foreign Visitors Arrivals Using Hybrid Model and Monte Carlo Simulation","volume":"21","author":"Danbatta","year":"2022","journal-title":"International J. Inf. Technol. Decis. Mak."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"103155","DOI":"10.1016\/j.annals.2021.103155","article-title":"Visitor Arrivals Forecasts amid COVID-19: A Perspective from the Asia and Pacific Team","volume":"88","author":"Qiu","year":"2021","journal-title":"Ann. Tour. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103149","DOI":"10.1016\/j.annals.2021.103149","article-title":"Forecasting Tourism Recovery amid COVID-19","volume":"87","author":"Zhang","year":"2021","journal-title":"Ann. Tour. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"012111","DOI":"10.1088\/1742-6596\/1366\/1\/012111","article-title":"Tourism Demand Forecasting\u2014A Review on the Variables and Models","volume":"1366","author":"Khaidi","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Box, G.E.P., Jenkins, G.M., and Reinsel, G.C. (2008). Time Series Analysis Forecasting and Control, John Wiley and Sons. [4th ed.].","DOI":"10.1002\/9781118619193"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Brockwell, P.J., and Davis, R.A. (2016). Introduction to Time Series and Forecasting, Springer International Publishing. Springer Texts in Statistics.","DOI":"10.1007\/978-3-319-29854-2"},{"key":"ref_16","first-page":"1","article-title":"Forecasting Seasonals and Trends by Exponentially Weighted Averages","volume":"52","author":"Holt","year":"1957","journal-title":"Carnegie Inst. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1287\/mnsc.6.3.324","article-title":"Forecasting Sales by Exponentially Weighted Moving Averages","volume":"6","author":"Winters","year":"1960","journal-title":"Manag. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","article-title":"Forecasting at Scale","volume":"72","author":"Taylor","year":"2018","journal-title":"Am. Stat."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning Representations by Back-Propagating Errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101882","DOI":"10.1016\/j.ribaf.2023.101882","article-title":"COVID-19 and Stock Returns: Evidence from the Markov Switching Dependence Approach","volume":"64","author":"Bouteska","year":"2023","journal-title":"Res. Int. Bus. 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