Weekly hotel occupancy forecasting of a tourism destination

Muzi Zhang, Junyi Li, Bing Pan, Gaojun Zhang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

The accurate forecasting of tourism demand is complicated by the dynamic tourism marketplace and its intricate causal relationships with economic factors. In order to enhance forecasting accuracy, we present a modified ensemble empirical mode decomposition (EEMD)- autoregressive integrated moving average (ARIMA) model, which dissects a time series into three intrinsic model functions (IMFs): high-frequency fluctuation, low-frequency fluctuation, and a trend; these three signals were then modeled using ARIMA methods. We used weekly hotel occupancy data from Charleston, South Carolina, USA as an empirical test case. The results showed that for medium-term forecasting (26 weeks) of hotel occupancy of a tourism destination, the modified EEMD-ARIMA model provides more accurate forecasting results with smaller standard deviations than the EEMD-ARIMA model, but further research is needed for validation.

Original languageEnglish (US)
Article number4351
JournalSustainability (Switzerland)
Volume10
Issue number12
DOIs
StatePublished - Nov 22 2018

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

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