Forecasting Destination Weekly Hotel Occupancy with Big Data

Accurate forecasting of future performance of hotels is needed so hospitality constituencies in specific destinations can benchmark their properties and better optimize operations. As competition increases, hotel managers have urgent need for accurate short-term forecasts. In this study, time series models including several tourism big data sources, including search engine queries, website traffic and weekly weather information, are tested in order to construct an accurate forecasting model of weekly hotel occupancy for a destination. The results show the superiority of ARMAX models with both search engine queries and website traffic data in accurate forecasting. Also, the results suggest that weekly dummies are superior to Fourier terms in capturing the hotel seasonality. The limitations of the inclusion of multiple big data sources are noted since the reduction in forecasting error is minimal.