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Beyond accuracy: The advantages of the k-nearest neighbor algorithm for hotel revenue management forecasting

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Listed:
  • Timothy Webb
  • Misuk Lee
  • Zvi Schwartz
  • Ira Vouk

Abstract

Revenue management (RM) systems forecast demand and optimize prices to maximize a hotel’s revenue. The RM function operates in coordination between a system and an analyst. Systems provide recommendations while analysts review the forecasts and prices to approve or make subjective adjustments. In many cases the recommendations are a “black box†with little insight regarding how recommendations are derived. This article proposes the k-Nearest Neighbor (k-NN) algorithm as a forecasting approach that can transition the “black box†to a “glass box.†The benefits of the k-NN are discussed in detail and compared with neural networks. The analysis is conducted on 35 hotels in partnership with a leading RM service provider. The results indicate similar performance for both techniques, leading to an important discussion on model evaluation outside of accuracy. In particular, the article discusses some of the unique advantages k-NN provides for the RM discipline.

Suggested Citation

  • Timothy Webb & Misuk Lee & Zvi Schwartz & Ira Vouk, 2024. "Beyond accuracy: The advantages of the k-nearest neighbor algorithm for hotel revenue management forecasting," Tourism Economics, , vol. 30(5), pages 1216-1236, August.
    • Handle: RePEc:sae:toueco:v:30:y:2024:i:5:p:1216-1236
    DOI: 10.1177/13548166231201199
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    References listed on IDEAS

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    4. Martin Petricek & Stepan Chalupa & David Melas, 2021. "Model of Price Optimization as a Part of Hotel Revenue Management—Stochastic Approach," Mathematics, MDPI, vol. 9(13), pages 1-16, July.
    5. Rennie, Nicola & Cleophas, Catherine & Sykulski, Adam M. & Dost, Florian, 2021. "Identifying and responding to outlier demand in revenue management," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1015-1030.
    6. Chris Tofallis, 2015. "A better measure of relative prediction accuracy for model selection and model estimation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(3), pages 524-524, March.
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