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Forecasting occupancy rate with Bayesian compression methods

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  • Assaf, A. George
  • Tsionas, Mike G.

Abstract

The curse of dimensionality is a challenge that researchers often face when dealing with large Vector Autoregressions (VARs). Different approaches have been proposed in the literature to address this issue. In this paper, we propose a new method based on the idea of compressed regression. In particular, we introduce two novel nonlinear compressed VARs to forecast the occupancy rate of hotels that compete within a narrow geographical area. We make the models more flexible through the introduction of neural networks, and compare their performance against several competing models. The empirical results show that the new compressed VARs outperform all other models, and their accuracy is preserved across nearly all forecast horizons from 1 to 36 months.

Suggested Citation

  • Assaf, A. George & Tsionas, Mike G., 2019. "Forecasting occupancy rate with Bayesian compression methods," Annals of Tourism Research, Elsevier, vol. 75(C), pages 439-449.
  • Handle: RePEc:eee:anture:v:75:y:2019:i:c:p:439-449
    DOI: 10.1016/j.annals.2018.12.009
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    Cited by:

    1. 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.
    2. Lingyu, Tang & Jun, Wang & Chunyu, Zhao, 2021. "Mode decomposition method integrating mode reconstruction, feature extraction, and ELM for tourist arrival forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    3. Tsionas, Mike G. & Assaf, A. George, 2021. "Compression in stochastic frontier models," Annals of Tourism Research, Elsevier, vol. 88(C).
    4. Zheng, Weimin & Huang, Liyao & Lin, Zhibin, 2021. "Multi-attraction, hourly tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 90(C).
    5. Dong Zhang & Chong Wu, 2023. "What online review features really matter? An explainable deep learning approach for hotel demand forecasting," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(9), pages 1100-1117, September.
    6. Fatemeh Binesh & Amanda Belarmino & Carola Raab, 2021. "A meta-analysis of hotel revenue management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(5), pages 546-558, October.

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