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

Gaojun Zhang, Jinfeng Wu, Bing Pan, Junyi Li, Minjie Ma, Muzi Zhang and Jian Wang
Additional contact information
Gaojun Zhang: Jinan University, China
Jinfeng Wu: Shaanxi Normal University, China
Bing Pan: Penn State University, USA
Junyi Li: Shaanxi Normal University, China
Minjie Ma: Xi’an Special Education School, China
Muzi Zhang: Shaanxi Normal University, China
Jian Wang: The Rainmaker Group, USA

Tourism Economics, 2017, vol. 23, issue 7, 1496-1514

Abstract: 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.

Keywords: ARIMA; EEMD; forecasting; hotels; occupancy; time series analysis (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/1354816617706852 (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:sae:toueco:v:23:y:2017:i:7:p:1496-1514

DOI: 10.1177/1354816617706852

Access Statistics for this article

More articles in Tourism Economics
Bibliographic data for series maintained by SAGE Publications ().