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Current Issues in Tourism Volume 25, 2022 - Issue 17
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Current Issues in Method and Practice

Forecasting hotel demand for revenue management using machine learning regression methods

Luis Nobre Pereiraa Escola Superior de Gestão, Hotelaria e Turismo & Research Centre for Tourism, Sustainability and Well-being, Universidade do Algarve, Faro, PortugalCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0003-0917-7163View further author information
ORCID Icon &
Vitor Cerqueirab Dalhousie University, Halifax, CanadaView further author information
Pages 2733-2750 | Received 15 Jan 2021, Accepted 21 Oct 2021, Published online: 17 Nov 2021

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Zunaidah Sulong, Mohammad Abdullah & Mohammad Ashraful Ferdous Chowdhury. (2023) Halal tourism demand and firm performance forecasting: new evidence from machine learning. Current Issues in Tourism 26:23, pages 3765-3781.
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Yi-Chung Hu. (2023) Tourism combination forecasting using a dynamic weighting strategy with change-point analysis. Current Issues in Tourism 26:14, pages 2357-2374.
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Erlong Zhao, Pei Du, Ernest Young Azaglo, Shouyang Wang & Shaolong Sun. (2023) Forecasting daily tourism volume: a hybrid approach with CEMMDAN and multi-kernel adaptive ensemble. Current Issues in Tourism 26:7, pages 1112-1131.
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Articles from other publishers (3)

Luciano Viverit, Cindy Yoonjoung Heo, Luís Nobre Pereira & Guido Tiana. (2023) Application of machine learning to cluster hotel booking curves for hotel demand forecasting. International Journal of Hospitality Management 111, pages 103455.
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Tao Hu & Haoyu Song. (2022) Analysis of Influencing Factors and Distribution Simulation of Budget Hotel Room Pricing Based on Big Data and Machine Learning from a Spatial Perspective. Sustainability 15:1, pages 617.
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M. Venkata Rakesh, S. Prasanna Kumar, Yogitha & R Aishwarya.. (2022) Hotel Booking Cancelation Prediction using ML algorithms. Hotel Booking Cancelation Prediction using ML algorithms.

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