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Forecasting at capacity: the bias of unconstrained forecasts in model evaluation

Abstract

Revenue management practices require accurate forecasts for optimal rate decisions, and therefore researchers and industry are keen on identifying the most accurate methods. This study is first to discuss the challenges of forecasting evaluation when predictions exceed capacity. Specifically, evaluators face two choices that are identified and defined, leading to the studies hypotheses. The empirical investigation confirms the importance of considering how to manage these predictions in the evaluation phase and demonstrates how the choice may sway overall accuracy measures and bias the results of model performance. The findings have important implications for capacity-based forecasting research and revenue management practice, since this previously undiscussed capacity-induced bias may alter the results of forecasting studies in academia, as well as the effectiveness of revenue management practices.

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References

  • Ampountolas, A. 2019. Forecasting hotel demand uncertainty using time series Bayesian VAR models. Tourism Economics 25 (5): 734–756.

    Article  Google Scholar 

  • Ampountolas, A., and M.P. Legg. 2021. A segmented machine learning modeling approach of social media for predicting occupancy. International Journal of Contemporary Hospitality Management 6: 2001–2021.

    Article  Google Scholar 

  • Anders, U., and O. Korn. 1999. Model selection in neural networks. Neural Networks 12 (2): 309–323.

    Article  Google Scholar 

  • Antonio, N., A. de Almeida, and L. Nunes. 2019. Big data in hotel revenue management: Exploring cancellation drivers to gain insights into booking cancellation behavior. Cornell Hospitality Quarterly 60 (4): 298–319.

    Article  Google Scholar 

  • Armstrong, J.S., and F. Collopy. 1992. Error measures for generalizing about forecasting methods: Empirical comparisons. International Journal of Forecasting 8 (1): 69–80.

    Article  Google Scholar 

  • Azadeh, S., R. Labib, and G. Savard. 2013. Railway demand forecasting in revenue management using neural networks. International Journal of Revenue Management 7 (1): 18–36.

    Article  Google Scholar 

  • Chen, C., and S. Kachani. 2007. Forecasting and optimisation for hotel revenue management. Journal of Revenue and Pricing Management 6 (3): 163–174.

    Article  Google Scholar 

  • Ellero, A., and P. Pellegrini. 2014. Are traditional forecasting models suitable for hotels in Italian cities? International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-02-2013-0107.

    Article  Google Scholar 

  • Fiori, A.M., and I. Foroni. 2020. Prediction accuracy for reservation-based forecasting methods applied in Revenue Management. International Journal of Hospitality Management 84: 102332.

    Article  Google Scholar 

  • Guo, P., B. Xiao, and J. Li. 2012. Unconstraining methods in revenue management systems: Research overview and prospects. Advances in Operations Research https://doi.org/10.1155/2012/270910.

    Article  Google Scholar 

  • Haensel, A., and G. Koole. 2011. Booking horizon forecasting with dynamic updating: A case study of hotel reservation data. International Journal of Forecasting 27 (3): 942–960.

    Article  Google Scholar 

  • Koupriouchina, L., J.P. van der Rest, and Z. Schwartz. 2014. On revenue management and the use of occupancy forecasting error measures. International Journal of Hospitality Management 41: 104–114.

    Article  Google Scholar 

  • Lee, M. 2018. Modeling and forecasting hotel room demand based on advance booking information. Tourism Management 66: 62–71.

    Article  Google Scholar 

  • Lee, M., X. Mu, and Y. Zhang. 2020. A machine learning approach to improving forecasting accuracy of hotel demand: A comparative analysis of neural networks and traditional models. Issues In Information Systems 21 (1): 12–21.

    Google Scholar 

  • Makridakis, S. 1993. Accuracy measures: Theoretical and practical concerns. International Journal of Forecasting 9 (4): 527–529.

    Article  Google Scholar 

  • Pan, B., and Y. Yang. 2017. Forecasting destination weekly hotel occupancy with big data. Journal of Travel Research 56 (7): 957–970.

    Article  Google Scholar 

  • Pereira, L.N. 2016. An introduction to helpful forecasting methods for hotel revenue management. International Journal of Hospitality Management 58: 13–23.

    Article  Google Scholar 

  • Phillips, R.L. 2005. Pricing and revenue optimization. Stanford: Stanford University Press.

    Book  Google Scholar 

  • Rajopadhye, M., M.B. Ghalia, P.P. Wang, T. Baker, and C.V. Eister. 2001. Forecasting uncertain hotel room demand. Information Sciences 132 (1–4): 1–11.

    Article  Google Scholar 

  • Schwartz, Z., and S. Hiemstra. 1997. Improving the accuracy of hotel reservations forecasting: Curves similarity approach. Journal of Travel Research 36 (1): 3–14.

    Article  Google Scholar 

  • Schwartz, Z. 1999. Monitoring the accuracy of multiple occupancy forecasts. Hospitality Review 17 (1): 4.

    Google Scholar 

  • Schwartz, Z., M. Uysal, T. Webb, and M. Altin. 2016. Hotel daily occupancy forecasting with competitive sets: A recursive algorithm. International Journal of Contemporary Hospitality Management. 28: 267–285.

    Article  Google Scholar 

  • Schwartz, Z., T. Webb, J.P.I. van der Rest, and L. Koupriouchina. 2021. Enhancing the accuracy of revenue management system forecasts: The impact of machine and human learning on the effectiveness of hotel occupancy forecast combinations across multiple forecasting horizons. Tourism Economics 27 (2): 273–291.

    Article  Google Scholar 

  • Sierag, D., J.P.V.D. Rest, G. Koole, R.V.D. Mei, and B. Zwart. 2017. A call for exploratory data analysis in revenue management forecasting: A case study of a small and independent hotel in The Netherlands. International Journal of Revenue Management 10 (1): 28–51.

    Article  Google Scholar 

  • Talluri, K.T., and G. Van Ryzin. 2004. The theory and practice of revenue management, vol. 1. Boston: Kluwer Academic Publishers.

    Book  Google Scholar 

  • Tse, T.S.M., and Y.T. Poon. 2015. Analyzing the use of an advance booking curve in forecasting hotel reservations. Journal of Travel & Tourism Marketing 32 (7): 852–869.

    Article  Google Scholar 

  • Wang, J., and A. Duggasani. 2020. Forecasting hotel reservations with long short-term memory-based recurrent neural networks. International Journal of Data Science and Analytics 9 (1): 77–94.

    Article  Google Scholar 

  • Weatherford, L. 2016a. The history of forecasting models in revenue management. Journal of Revenue and Pricing Management 15 (3): 212–221.

    Article  Google Scholar 

  • Weatherford, L. 2016b. The history of unconstraining models in revenue management. Journal of Revenue and Pricing Management 15 (3): 222–228.

    Article  Google Scholar 

  • Weatherford, L.R., and S.E. Kimes. 2003. A comparison of forecasting methods for hotel revenue management. International Journal of Forecasting 19 (3): 401–415.

    Article  Google Scholar 

  • Weatherford, L.R., S.E. Kimes, and D.A. Scott. 2001. Forecasting for hotel revenue management: Testing aggregation against disaggregation. Cornell Hotel and Restaurant Administration Quarterly 42 (4): 53–64.

    Article  Google Scholar 

  • Weatherford, L.R., and S. Pölt. 2002. Better unconstraining of airline demand data in revenue management systems for improved forecast accuracy and greater revenues. Journal of Revenue and Pricing Management 1 (3): 234–254.

    Article  Google Scholar 

  • Webb, T., Z. Schwartz, Z. Xiang, and M. Singal. 2020. Revenue management forecasting: The resiliency of advanced booking methods given dynamic booking windows. International Journal of Hospitality Management 89: 102590.

    Article  Google Scholar 

  • Zakhary, A., N. El Gayar, and A.F. Atiya. 2008. A comparative study of the pickup method and its variations using a simulated hotel reservation data. ICGST International Journal on Artificial Intelligence and Machine Learning 8: 15–21.

    Google Scholar 

  • Zhang, G., J. Wu, B. Pan, J. Li, M. Ma, M. Zhang, and J. Wang. 2017. Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model. Tourism Economics 23 (7): 1496–1514.

    Article  Google Scholar 

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Correspondence to Timothy Webb.

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Webb, T. Forecasting at capacity: the bias of unconstrained forecasts in model evaluation. J Revenue Pricing Manag 21, 645–656 (2022). https://doi.org/10.1057/s41272-022-00389-4

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  • DOI: https://doi.org/10.1057/s41272-022-00389-4

Keywords

  • Hotels
  • Revenue management
  • Forecasting
  • Evaluation