A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model
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...
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MDPI AG
2025-05-01
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| author | Geun-Cheol Lee |
| author_facet | Geun-Cheol Lee |
| author_sort | Geun-Cheol Lee |
| collection | DOAJ |
| description | 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%. |
| format | Article |
| id | doaj-art-bb3b2de2389143c7a7ad036ff674a5c7 |
| institution | DOAJ |
| issn | 2306-5729 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Data |
| spelling | doaj-art-bb3b2de2389143c7a7ad036ff674a5c72025-08-20T03:14:36ZengMDPI AGData2306-57292025-05-011057310.3390/data10050073A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX ModelGeun-Cheol Lee0College of Business, Konkuk University, Seoul 05029, Republic of KoreaTourism 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%.https://www.mdpi.com/2306-5729/10/5/73tourism demand forecastingSARIMAXexogenous variablesGoogle Trendstime-series analysispost-COVID-19 |
| spellingShingle | Geun-Cheol Lee A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model Data tourism demand forecasting SARIMAX exogenous variables Google Trends time-series analysis post-COVID-19 |
| title | A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model |
| title_full | A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model |
| title_fullStr | A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model |
| title_full_unstemmed | A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model |
| title_short | A Data-Driven Approach to Tourism Demand Forecasting: Integrating Web Search Data into a SARIMAX Model |
| title_sort | data driven approach to tourism demand forecasting integrating web search data into a sarimax model |
| topic | tourism demand forecasting SARIMAX exogenous variables Google Trends time-series analysis post-COVID-19 |
| url | https://www.mdpi.com/2306-5729/10/5/73 |
| work_keys_str_mv | AT geuncheollee adatadrivenapproachtotourismdemandforecastingintegratingwebsearchdataintoasarimaxmodel AT geuncheollee datadrivenapproachtotourismdemandforecastingintegratingwebsearchdataintoasarimaxmodel |