TY - JOUR
T1 - Weekly hotel occupancy forecasting of a tourism destination
AU - Zhang, Muzi
AU - Li, Junyi
AU - Pan, Bing
AU - Zhang, Gaojun
N1 - Funding Information:
Qing Zhu of the International Business School of Shaanxi Normal University offered tremendous guidance and help in research methodology. This project was jointly funded by Natural Science Foundation Projects No. 41571135, No. 41428101, and Jinan University, Shenzhen Tourism College OSP #193942 and #193940.
Funding Information:
Funding: This project was jointly funded by Natural Science Foundation Projects No. 41571135, No. 41428101, and Jinan University, Shenzhen Tourism College OSP #193942 and #193940.
Publisher Copyright:
© 2018 by the authors.
PY - 2018/11/22
Y1 - 2018/11/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85057135522&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057135522&partnerID=8YFLogxK
U2 - 10.3390/su10124351
DO - 10.3390/su10124351
M3 - Article
AN - SCOPUS:85057135522
SN - 2071-1050
VL - 10
JO - Sustainability (Switzerland)
JF - Sustainability (Switzerland)
IS - 12
M1 - 4351
ER -