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Gaussian processes for daily demand prediction in tourism planning

Wai Tsang (UGent) and Dries Benoit (UGent)
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Abstract
This study proposes Gaussian processes to forecast daily hotel occupancy at a city level. Unlike other studies in the tourism demand prediction literature, the hotel occupancy rate is predicted on a daily basis and 45 days ahead of time using online hotel room price data. A predictive framework is introduced that highlights feature extraction and selection of the independent variables. This approach shows that the dependence on internal hotel occupancy data can be removed by making use of a proxy measure for hotel occupancy rate at a city level. Six forecasting methods are investigated, including linear regression, autoregressive integrated moving average and recent machine learning methods. The results indicate that Gaussian processes offer the best tradeoff between accuracy and interpretation by providing prediction intervals in addition to point forecasts. It is shown how the proposed framework improves managerial decision making in tourism planning.
Keywords
Management Science and Operations Research, Strategy and Management, Statistics, Probability and Uncertainty, Modelling and Simulation, Computer Science Applications

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Please use this url to cite or link to this publication:

MLA
Tsang, Wai, and Dries Benoit. “Gaussian Processes for Daily Demand Prediction in Tourism Planning.” JOURNAL OF FORECASTING, 2019.
APA
Tsang, W., & Benoit, D. (2019). Gaussian processes for daily demand prediction in tourism planning. JOURNAL OF FORECASTING.
Chicago author-date
Tsang, Wai, and Dries Benoit. 2019. “Gaussian Processes for Daily Demand Prediction in Tourism Planning.” JOURNAL OF FORECASTING.
Chicago author-date (all authors)
Tsang, Wai, and Dries Benoit. 2019. “Gaussian Processes for Daily Demand Prediction in Tourism Planning.” JOURNAL OF FORECASTING.
Vancouver
1.
Tsang W, Benoit D. Gaussian processes for daily demand prediction in tourism planning. JOURNAL OF FORECASTING. 2019;
IEEE
[1]
W. Tsang and D. Benoit, “Gaussian processes for daily demand prediction in tourism planning,” JOURNAL OF FORECASTING, 2019.
@article{8642361,
  abstract     = {This study proposes Gaussian processes to forecast daily hotel occupancy at a city level. Unlike other studies in the tourism demand prediction literature, the hotel occupancy rate is predicted on a daily basis and 45 days ahead of time using online hotel room price data. A predictive framework is introduced that highlights feature extraction and selection of the independent variables. This approach shows that the dependence on internal hotel occupancy data can be removed by making use of a proxy measure for hotel occupancy rate at a city level. Six forecasting methods are investigated, including linear regression, autoregressive integrated moving average and recent machine learning methods. The results indicate that Gaussian processes offer the best tradeoff between accuracy and interpretation by providing prediction intervals in addition to point forecasts. It is shown how the proposed framework improves managerial decision making in tourism planning.},
  author       = {Tsang, Wai and Benoit, Dries},
  issn         = {0277-6693},
  journal      = {JOURNAL OF FORECASTING},
  keywords     = {Management Science and Operations Research,Strategy and Management,Statistics,Probability and Uncertainty,Modelling and Simulation,Computer Science Applications},
  language     = {eng},
  pages        = {18},
  title        = {Gaussian processes for daily demand prediction in tourism planning},
  url          = {http://dx.doi.org/10.1002/for.2644},
  year         = {2019},
}

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