Forecasting occupancy rate with Bayesian compression methods
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DOI: 10.1016/j.annals.2018.12.009
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Cited by:
- 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.
- 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).
- Tsionas, Mike G. & Assaf, A. George, 2021. "Compression in stochastic frontier models," Annals of Tourism Research, Elsevier, vol. 88(C).
- Zheng, Weimin & Huang, Liyao & Lin, Zhibin, 2021. "Multi-attraction, hourly tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 90(C).
- 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.
- 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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Keywords
Large Vector Autoregressions (VARs); Compression Methods; Bayesian; Neural networks; Hotel occupancy rate;All these keywords.
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