Optimizing SARIMAX Model with Big Data to Predict Gaming Tourism Destination Demand

Chong Fo Lei, Fusheng Chen and Chia Wei Chu ()
Additional contact information
Chong Fo Lei: Faculty of Data Science, City University of Macau, Avenida Padre Tomás Pereira Taipa, Macau, China
Fusheng Chen: Faculty of Data Science, City University of Macau, Avenida Padre Tomás Pereira Taipa, Macau, China
Chia Wei Chu: Faculty of Data Science, City University of Macau, Avenida Padre Tomás Pereira Taipa, Macau, China

Mathematics, 2025, vol. 13, issue 20, 1-17

Abstract: Tourism demand forecasting has evolved into a wide variety of models, including time-series models that incorporate economic, environmental, and behavioral factors. Macao, one of the world’s most profitable gaming destinations, finds that gaming revenue is highly related to tourist arrivals. A forecast model for gaming tourism is essential for accurately predicting tourist arrivals. The challenge with ARIMA-type models is optimizing parameter selection in order to improve the accuracy of tourism demand forecasts. In this study, an enhanced version of SARIMAX, called SARIMAX-E, was developed to identify the most effective parameter combinations. By integrating data related to gaming revenue, weather, transportation, currency exchange rate, holidays, and seasonality into a single forecast model, this study examined the performance of different forecasting models, including the proposed SARIMAX-E model; ARIMA-type models (ARIMA, SARIMA, ARIMAX); and machine learning models (Transformer, LTSM, Random Forests, XGBoost). The results showed that the ARIMA family of models, including SARIMAX-E, ARIMAX, and SARIMA, was particularly well suited to tourism demand forecasting, as its members consistently ranked among the top performers in terms of error metrics. By applying multi-step predictions, LSTM outperforms most conventional approaches. Compared with all other models, the SARIMAX-E performed the best after applying the additional parameter grid.

Keywords: time-series forecasting; SARIMAX; big data; gaming tourism (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/20/3276/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/20/3276/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:20:p:3276-:d:1770575

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().