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Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Author

Listed:
  • Binru Zhang

    (School of Finance and Economics, Yangtze Normal University, Chongqing 408100, China)

  • Yulian Pu

    (School of Management, Yangtze Normal University, Chongqing 408100, China)

  • Yuanyuan Wang

    (School of Economics and Management, Hainan Normal University, Haikou 571158 China)

  • Jueyou Li

    (School of Mathematcal Science, Chongqing Normal University, Chongqing 401331, China)

Abstract

Accurate forecasting of the hotel accommodation demands is extremely critical to the sustainable development of tourism-related industries. In view of the ever-increasing tourism data, this paper constructs a deep learning framework to handle the prediction problem in the hotel accommodation demands. Taking China’s Hainan province as an empirical example, the internet search index is used from August 2008 to May 2019 to forecast the overnight passenger flows for hotels accommodation in Hainan Province, China. Forecasting results indicate that compared to benchmark models, the constructed forecasting method can effectively simulate dynamic characteristics of the overnight passenger flows for the hotel accommodation and significantly improve the forecasting performance of the model. Forecasting results can provide necessary references for decision-making in tourism-related industries, and this forecasting framework can also be extended to other similar complex time series forecasting problems.

Suggested Citation

  • Binru Zhang & Yulian Pu & Yuanyuan Wang & Jueyou Li, 2019. "Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index," Sustainability, MDPI, vol. 11(17), pages 1-14, August.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4708-:d:262036
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    References listed on IDEAS

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    3. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
    4. Rashad Aliyev & Sara Salehi & Rafig Aliyev, 2019. "Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting," Sustainability, MDPI, vol. 11(3), pages 1-13, February.
    5. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    6. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
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    Cited by:

    1. 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.
    2. 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.
    3. Haodong Sun & Yang Yang & Yanyan Chen & Xiaoming Liu & Jiachen Wang, 2023. "Tourism demand forecasting of multi-attractions with spatiotemporal grid: a convolutional block attention module model," Information Technology & Tourism, Springer, vol. 25(2), pages 205-233, June.
    4. Eunjeong Choi & Soohwan Cho & Dong Keun Kim, 2020. "Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability," Sustainability, MDPI, vol. 12(3), pages 1-14, February.
    5. Lucie Severová & Karel Šrédl & Marie Prášilová & Roman Svoboda & Alexandr Soukup & Marek Dvořák & Jitka Prachařová, 2021. "Change in the Structure of the Accommodation Capacity of the Czech Hotel Industry under Conditions of Economic Globalization," Sustainability, MDPI, vol. 13(16), pages 1-24, August.

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