2023
DOI: 10.4108/eetct.v9i31.2986
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A Review of the Methods and Techniques Used in Tourism Demand Forecasting

Abstract: The purpose of this paper is to discuss the methodology and results of researchers who conducted a study concerning the forecasting of tourism demand. In more detail, this study aims to examine and assess various studies about search engines, web traffic data, and social media data, specifically. Using an extensive database of indexed articles, we conducted the review with the goal of providing a solid understanding of the literature. The findings of our study revealed that few researchers integrate different … Show more

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Cited by 5 publications
(2 citation statements)
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References 28 publications
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“…Cao (2022) thoroughly examined vector autoregressive (VAR) models and their applications in tourism demand research, showcasing the versatility of VAR models in capturing interrelationships between tourism and economic variables. Al Jassim et al (2022) assessed studies on forecasting tourism demand, emphasizing the importance of integrating diverse data sources and inspiring future research. Dowlut and Gobin-Rahimbux (2023) explored deep learning techniques for occupancy rate prediction in the hospitality industry, highlighting the adoption of Long Short-Term Memory (LSTM) models and underscoring the significance of hybrid models for enhanced accuracy.…”
Section: Screening Resultsmentioning
confidence: 99%
“…Cao (2022) thoroughly examined vector autoregressive (VAR) models and their applications in tourism demand research, showcasing the versatility of VAR models in capturing interrelationships between tourism and economic variables. Al Jassim et al (2022) assessed studies on forecasting tourism demand, emphasizing the importance of integrating diverse data sources and inspiring future research. Dowlut and Gobin-Rahimbux (2023) explored deep learning techniques for occupancy rate prediction in the hospitality industry, highlighting the adoption of Long Short-Term Memory (LSTM) models and underscoring the significance of hybrid models for enhanced accuracy.…”
Section: Screening Resultsmentioning
confidence: 99%
“…Literature indicates that adding explanatory variables to univariate time series models may enhance forecasts (Jassim et al, 2023). However, the limited availability of data types that can be used as explanatory variables in traditional forecasting models makes it challenging to implement these models.…”
Section: Machine Learning Models For Tourism Demand Forecastingmentioning
confidence: 99%