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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Gaojun Zhang & Jinfeng Wu & Bing Pan & Junyi Li & Minjie Ma & Muzi Zhang & Jian Wang, 2017. "Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model," Tourism Economics, , vol. 23(7), pages 1496-1514, November.
    • Handle: RePEc:sae:toueco:v:23:y:2017:i:7:p:1496-1514
    DOI: 10.1177/1354816617706852
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    References listed on IDEAS

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    Cited by:

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    2. Xie, Gang & Qian, Yatong & Wang, Shouyang, 2020. "A decomposition-ensemble approach for tourism forecasting," Annals of Tourism Research, Elsevier, vol. 81(C).
    3. Vatsa, Puneet, 2020. "Comovement amongst the demand for New Zealand tourism," Annals of Tourism Research, Elsevier, vol. 83(C).
    4. 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.
    5. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    6. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    7. 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.
    8. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.

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