Forecasting Destination Weekly Hotel Occupancy with Big Data

Bing Pan, Yang Yang

Research output: Contribution to journalArticlepeer-review

166 Scopus citations


Hospitality constituencies need accurate forecasting of future performance of hotels in specific destinations to 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 incorporating 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.

Original languageEnglish (US)
Pages (from-to)957-970
Number of pages14
JournalJournal of Travel Research
Issue number7
StatePublished - Sep 1 2017

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Transportation
  • Tourism, Leisure and Hospitality Management


Dive into the research topics of 'Forecasting Destination Weekly Hotel Occupancy with Big Data'. Together they form a unique fingerprint.

Cite this