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

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.