Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model

Gaojun Zhang, Jinfeng Wu, Bing Pan, Junyi Li, Minjie Ma, Muzi Zhang, Jian Wang

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Predicting daily occupancy is extremely important for the revenue management of individual hotels. However, daily occupancy can fluctuate widely and is difficult to forecast accurately based on existing forecasting methods. In this article, ensemble empirical mode decomposition (EEMD) - a novel method - is introduced, and an individual hotel is chosen to test the effectiveness of EEMD in combination with an autoregressive integrated moving average (ARIMA). Result shows that this novel method, EEMD-ARIMA, can improve forecasting accuracy compared to the popular ARIMA method, especially for short-term forecasting.

Original languageEnglish (US)
Pages (from-to)1496-1514
Number of pages19
JournalTourism Economics
Issue number7
StatePublished - Nov 1 2017

All Science Journal Classification (ASJC) codes

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


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