Tourism demand forecasting using ensembles of regression trees

Cankurt, Selçuk

doi:10.1109/is.2016.7737388

Cited by 12 publications

(12 citation statements)

References 35 publications

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“…Ensemble methods are independent of specific base models and thus robust in forecasting performance (Zhao et al, 2019). Cankurt (2016) employed regression tree-based ensemble method for tourism demand forecasting in Turkey. Y.…”

Section: Model-oriented Meta-methodsmentioning

confidence: 99%

Predictivity of tourism demand data

Zhang

Muskat

et al. 2021

Annals of Tourism Research

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Section: Model-oriented Meta-methodsmentioning

confidence: 99%

Predictivity of tourism demand data

Zhang

Muskat

et al. 2021

Annals of Tourism Research

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“…Cai, Lu, and Zhang (2009) found that the generic support vector regression (SVR) algorithm exhibits a more satisfactory performance and requires fewer parameters than the ARIMA methods. Cankurt (2016) used the regression tree for Turkish tourism demand forecasting. Artificial neural networks (ANNs) are also adopted as a nonlinear forecasting model in tourism demand since the early 1990s.…”

Section: Related Workmentioning

confidence: 99%

Tourism Demand Forecasting: A Decomposed Deep Learning Approach

Zhang

Muskat

et al. 2020

Journal of Travel Research

103

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Tourism planners rely on accurate demand forecasting. However, despite numerous advancements, crucial methodological issues remain unaddressed. This study aims to further improve the modeling accuracy and advance the artificial intelligence (AI)-based tourism demand forecasting methods. Deep learning models that predict tourism demand are often highly complex and encounter overfitting, which is mainly caused by two underlying problems: (1) access to limited data volumes and (2) additional explanatory variable requirement. To address these issues, we use a decomposition method that achieves high accuracy in short- and long-term AI-based forecasting models. The proposed method effectively decomposes the data and increases accuracy without additional data requirement. In conclusion, this study alleviates the overfitting issue and provides a methodological contribution by proposing a highly accurate deep learning method for AI-based tourism demand modeling.

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“…In 2016, [153] used SVMs alongside kernel logistic regression to prove that is possible to generate models to predict booking cancellations with high accuracy. In [82] various regression methods, including SVR, were analyzed to discover that SVR outperforms multiple linear regression and multi-layer perceptron regression models in tourism demand forecasting. In [154] SVR is also hybridized with a seasonal component and optimized by the fruit fly optimization algorithm, yielding positive results that position this model as a feasible tourism forecast solution.…”

Section: Tourism Demand Forecastingmentioning

confidence: 99%

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

Guerra-Montenegro

Sánchez-Medina

Laña

et al. 2021

Applied Soft Computing

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This research work presents a detailed survey about Computational Intelligence (CI) applied to various Hotel and Travel Industry areas. Currently, the hospitality industry's interest in data science is growing exponentially because of their expected margin of profit growth. In order to provide precise state of the art content, this survey analyzes more than 160 research works from which a detailed categorization and taxonomy have been produced. We have studied the different approaches on the various forecasting methods and subareas where CI is currently being used. This research work also shows an actual distribution of these research efforts in order to enhance the understanding of the reader about this topic and to highlight unexploited research niches. A set of guidelines and recommendations for future research areas and promising applications are also presented in a final section.

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Tourism demand forecasting using ensembles of regression trees

Cited by 12 publications

References 35 publications

Predictivity of tourism demand data

Predictivity of tourism demand data

Tourism Demand Forecasting: A Decomposed Deep Learning Approach

Computational Intelligence in the hospitality industry: A systematic literature review and a prospect of challenges

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