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Weekly Hotel Occupancy Forecasting of a Tourism Destination

Author

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  • Muzi Zhang

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710062, China
    Qionglai Prefectural Bureau of Culture, Sport, Radio and TV, Press and Publication, and Tourism, Chengdu 611530, China)

  • Junyi Li

    (School of Geography and Tourism, Shaanxi Normal University, Xi’an 710062, China
    Shaanxi Key Laboratory of Tourism Informatics, Xi’an 710062, China)

  • Bing Pan

    (Department of Recreation, Park, and Tourism Management, College of Health and Human Development, Penn State University, University Park, PA 16801, USA)

  • Gaojun Zhang

    (Shenzhen Tourism College, Jinan University, Shenzhen 518055, China)

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.

Suggested Citation

  • Muzi Zhang & Junyi Li & Bing Pan & Gaojun Zhang, 2018. "Weekly Hotel Occupancy Forecasting of a Tourism Destination," Sustainability, MDPI, vol. 10(12), pages 1-17, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:12:p:4351-:d:184796
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    References listed on IDEAS

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    Cited by:

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    2. Chengyuan Zhang & Fuxin Jiang & Shouyang Wang & Shaolong Sun, 2020. "A New Decomposition Ensemble Approach for Tourism Demand Forecasting: Evidence from Major Source Countries," Papers 2002.09201, arXiv.org.
    3. Giovanni De Luca & Monica Rosciano, 2020. "Quantile Dependence in Tourism Demand Time Series: Evidence in the Southern Italy Market," Sustainability, MDPI, vol. 12(8), pages 1-18, April.
    4. Juan Luis Jiménez & Armando Ortuño & Jorge V. Pérez-Rodríguez, 2022. "How does AirBnb affect local Spanish tourism markets?," Empirical Economics, Springer, vol. 62(5), pages 2515-2545, May.
    5. Tianxiang Zheng & Shaopeng Liu & Zini Chen & Yuhan Qiao & Rob Law, 2020. "Forecasting Daily Room Rates on the Basis of an LSTM Model in Difficult Times of Hong Kong: Evidence from Online Distribution Channels on the Hotel Industry," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    6. Oscar Trull & Angel Peiró-Signes & J. Carlos García-Díaz, 2019. "Electricity Forecasting Improvement in a Destination Using Tourism Indicators," Sustainability, MDPI, vol. 11(13), pages 1-16, July.
    7. Nyoni, Thabani, 2019. ""Incredible India"-an empirical confrimation from the Box-Jenkins ARIMA technique," MPRA Paper 96909, University Library of Munich, Germany.
    8. José Antonio Cava Jiménez & María Genoveva Millán Vázquez de la Torre & Ricardo Hernández Rojas, 2019. "Analysis of the Tourism Demand for Iberian Ham Routes in Andalusia (Southern Spain): Tourist Profile," Sustainability, MDPI, vol. 11(16), pages 1-21, August.

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