1,788
Views
6
CrossRef citations to date
0
Altmetric
Current Issues in Method and Practice

Forecasting hotel demand for revenue management using machine learning regression methods

ORCID Icon &
Pages 2733-2750 | Received 15 Jan 2021, Accepted 21 Oct 2021, Published online: 17 Nov 2021

References

  • Ampountolas, A. (2019). Forecasting hotel demand uncertainty using time series Bayesian VAR models. Tourism Economics, 25(5), 734–756. https://doi.org/10.1177/1354816618801741
  • Antonio, N., Almeida, A., & Nunes, L. (2019). Big data in hotel revenue management: Exploring cancellation drivers to gain insights into booking cancellation behavior. Cornell Hospitality Quarterly, 60(4), 298–319. https://doi.org/10.1177/1938965519851466
  • Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: A decomposition approach to forecasting. International Journal of Forecasting, 16(4), 521–530. https://doi.org/10.1016/S0169-2070(00)00066-2
  • Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. John Wiley & Sons.
  • Carbonneau, R., Laframboise, K., & Vahidov, R. (2008). Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), 1140–1154. https://doi.org/10.1016/j.ejor.2006.12.004
  • Cerqueira, V., Torgo, L., Oliveira, M., & Pfahringer, B. (2017). Dynamic and heterogeneous ensembles for time series forecasting. In DSAA 2017 (Ed.), Proceedings - 2017 IEEE International Conference on Data Science and Advanced Analytics (pp. 242–251). IEEE.
  • Cerqueira, V., Torgo, L., Pinto, F., & Soares, C. (2019). Arbitrage of forecasting experts. Machine Learning, 108(6), 913–944. https://doi.org/10.1007/s10994-018-05774-y
  • Cerqueira, V., Torgo, L., & Soares, C. (2019). Machine learning vs statistical methods for time series forecasting: Size matters. arXiv preprint arXiv:1909.13316.
  • Chatfield, C. (2013). The analysis of time series: An introduction (6th ed.). CRC Press.
  • Chen, C., & Kachani, S. (2007). Forecasting and optimisation for hotel revenue management. Journal of Revenue and Pricing Management, 6(3), 163–174. https://doi.org/10.1057/palgrave.rpm.5160082
  • Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., Li, M., Xie, J., Lin, M., Geng, Y., & Li, Y. (2019). xgboost: Extreme gradient boosting (R package version 0.82.1). https://CRAN.R-project.org/package=xgboost
  • Chiang, W.-C., Chen, J. C. H., & Xu, X. (2007). An overview of research on revenue management: Current issues and future research. International Journal of Revenue Management, 1(1), 97–128. https://doi.org/10.1504/IJRM.2007.011196
  • Cox, D. R., & Stuart, A. (1955). Some quick sign tests for trend in location and dispersion. Biometrika, 42(1/2), 80–95. https://doi.org/10.2307/2333424
  • Cross, R. G., Higbie, J. A., & Cross, D. Q. (2009). Revenue management's renaissance: A rebirth of the art and science of profitable revenue generation. Cornell Hospitality Quarterly, 50(1), 56–81. https://doi.org/10.1177/1938965508328716
  • Dawid, A. P. (1984). Present position and potential developments: Some personal views statistical theory the prequential approach. Journal of the Royal Statistical Society: Series A (General), 147(2), 278–290. https://doi.org/10.2307/2981683
  • De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513–1527. https://doi.org/10.1198/jasa.2011.tm09771
  • Egan, D., & Haynes, N. C. (2019). Manager perceptions of big data reliability in hotel revenue management decision making. International Journal of Quality & Reliability Management, 36(1), 25–39. https://doi.org/10.1108/IJQRM-02-2018-0056
  • Erdem, M., & Jiang, L. (2016). An overview of hotel revenue management research and emerging key patterns in the third millennium. Journal of Hospitality and Tourism Technology, 7(3), 300–312. https://doi.org/10.1108/JHTT-10-2014-0058
  • Faraway, J., & Chatfield, C. (1998). Time series forecasting with neural networks: A comparative study using the air line data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 47(2), 231–250. https://doi.org/10.1111/1467-9876.00109
  • Fiig, T., Weatherford, L. R., & Wittman, M. D. (2019). Can demand forecast accuracy be linked to airline revenue? Journal of Revenue and Pricing Management, 18(4), 291–305. https://doi.org/10.1057/s41272-018-00174-2
  • Fiori, A. M., & Foroni, I. (2020). Prediction accuracy for reservation-based forecasting methods applied in revenue management. International Journal of Hospitality Management, 84(2), Article 102332. https://doi.org/10.1016/j.ijhm.2019.102332
  • Friedman, J. H. (1984). Smart user's guide (Technical Report). Stanford Univ CA Lab for Computational Statistics.
  • Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.
  • Friedman, J. H., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 1–22. https://doi.org/10.18637/jss.v033.i01
  • Gardner Jr., E. S. (2006). Exponential smoothing: The state of the art–Part II. International Journal of Forecasting, 22(3), 637–666. https://doi.org/10.1016/j.ijforecast.2006.03.005
  • Gneiting, T., & Katzfuss, M. (2014). Probabilistic forecasting. Annual Review of Statistics and Its Application, 1(1), 125–151. https://doi.org/10.1146/statistics.2013.1.issue-1
  • Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3), 443–473. https://doi.org/10.1016/j.ijforecast.2006.01.001
  • Guerrero, V. M. (1993). Time-series analysis supported by power transformations. Journal of Forecasting, 12(1), 37–48. https://doi.org/10.1002/(ISSN)1099-131X
  • Haensel, A., & Koole, G. (2011). Booking horizon forecasting with dynamic updating: A case study of hotel reservation data. International Journal of Forecasting, 27(3), 942–960. https://doi.org/10.1016/j.ijforecast.2010.10.004
  • Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20(1), 5–10. https://doi.org/10.1016/j.ijforecast.2003.09.015
  • Hyndman, R. J., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O'Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., & Yasmeen, F. (2021). Forecast: Forecasting functions for time series and linear models (R package version 8.15). https://pkg.robjhyndman.com/forecast/
  • Hyndman, R. J., Koehler, A. B., Ord, J. K., & Snyder., R. D. (2008). Forecasting with exponential smoothing: The state space approach. Springer Science & Business Media.
  • Ivanov, S. (2014). Hotel revenue management: From theory to practice. Zangador.
  • Ivanov, S., & Zhechev, V. (2012). Hotel revenue management – a critical literature review. Turizam: Medunarodni Znanstveno-Stručni Časopis, 60(2), 175–197.
  • Karatzoglou, A., Smola, A., Hornik, K., & Zeileis, A. (2004). kernlab – an S4 package for kernel methods in R. Journal of Statistical Software, 11(9), 1–20. https://doi.org/10.18637/jss.v011.i09
  • Kennel, M. B., Brown, R., & Abarbanel, H. D. I. (1992). Determining embedding dimension for phase-space reconstruction using a geometrical construction. Physical Review A, 45(6), 3403–3411. https://doi.org/10.1103/PhysRevA.45.3403
  • Kimes, S. E. (1989). The basics of yield management. Cornell Hotel and Restaurant Administration Quarterly, 30(3), 14–19. https://doi.org/10.1177/001088048903000309
  • Koupriouchina, L., van der Rest, J.-P., & Schwartz, Z. (2014). On revenue management and the use of occupancy forecasting error measures. International Journal of Hospitality Management, 41(3), 104–114. https://doi.org/10.1016/j.ijhm.2014.05.002
  • Kourentzes, N., Saayman, A., Jean-Pierre, P., Provenzano, D., Sahli, M., Seetaram, N., & Volo, S. (2021). Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team. Annals of Tourism Research, 88(4), 103197. https://doi.org/10.1016/j.annals.2021.103197
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
  • Lee, A. O. (1990). Airline reservations forecasting: Probabilistic and statistical models of the booking process (Technical Report). Flight Transportation Laboratory, Dept. of Aeronautics and Astronautics.
  • Lee, J., & Mark, R. G. (2010). An investigation of patterns in hemodynamic data indicative of impending hypotension in intensive care. Biomedical Engineering Online, 9(1), 62. https://doi.org/10.1186/1475-925X-9-62
  • Lee, M. (2012, November 17). Improving the forecasting accuracy of hotel arrivals: A new nonho-mogeneous Poisson approach [Paper presentation]. Informs. Vol. 3, Annual Meeting of the 43rd Decision Sciences Institute Annual Meeting, San Francisco, CA, United States.
  • Li, G., Wu, D. C., Zhou, M., & Liu, A. (2019). The combination of interval forecasts in tourism. Annals of Tourism Research, 75(3), 363–378. https://doi.org/10.1016/j.annals.2019.01.010
  • Lim, C., & Chan, F. (2011). An econometric analysis of hotel? Motel room nights in New Zealand with stochastic seasonality. International Journal of Revenue Management, 5(1), 63–83. https://doi.org/10.1504/IJRM.2011.038619
  • Lim, C., Chang, C., & McAleer, M. (2009). Forecasting h(m)otel guest nights in New Zealand. International Journal of Hospitality Management, 28(2), 228–235. https://doi.org/10.1016/j.ijhm.2008.08.001
  • Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and machine learning forecasting methods: Concerns and ways forward. PloS One, 13(3), e0194889. https://doi.org/10.1371/journal.pone.0194889
  • Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2021). Machine learning advances for time series forecasting. Journal of Economic Surveys, 1–36. https://doi.org/10.1111/joes.12429
  • McCullagh, P. (2019). Generalized linear models. Routledge.
  • Mevik, B.-H., Wehrens, R., & Liland, K. H. (2016). pls: Partial least squares and principal component regression (R package version 2.6-0). https://CRAN.R-project.org/package=pls
  • Milborrow, S. (2012). Earth: Multivariate adaptive regression spline models (R package version 3.2-3). http://www.milbo.users.sonic.net/earth/
  • Pan, B., & Yang, Y. (2017). Forecasting destination weekly hotel occupancy with big data. Journal of Travel Research, 56(7), 957–970. https://doi.org/10.1177/0047287516669050
  • Pereira, L. N. (2016). An introduction to helpful forecasting methods for hotel revenue management. International Journal of Hospitality Management, 58(2), 13–23. https://doi.org/10.1016/j.ijhm.2016.07.003
  • Phumchusri, N., & Ungtrakul, P. (2020). Hotel daily demand forecasting for high-frequency and complex seasonality data: a case study in Thailand. Journal of Revenue and Pricing Management, 19(1), 8–25. https://doi.org/10.1057/s41272-019-00221-6
  • Pölt, S. (1998). Forecasting is difficult – especially if it refers to the future. In AGIFORS (Ed.), Agifors-Reservations and Yield Management Study Group Meeting Proceedings (pp. 61–91).
  • Quinlan, J. R. (1993). Combining instance-based and model-based learning. In Utgoff (Ed.), Proceedings of the Tenth International Conference on Machine Learning (pp. 236–243).
  • Rajopadhye, M., Ghalia, M. B., W., P. P., Baker, T., & Eister, C. V. (2001). Forecasting uncertain hotel room demand. Information Sciences, 132(1–4), 1–11. https://doi.org/10.1016/S0020-0255(00)00082-7
  • Rasmussen, C. E. (2003). Gaussian processes in machine learning. In O. Bousquet, U. von Luxburg & G. Rätsch (Eds.), Advanced Lectures on Machine Learning. ML 2003. Lecture Notes in Computer Science, vol 3176 (pp. 63–71). Springer. https://doi.org/10.1007/978-3-540-28650-9_4
  • Ridgeway, G. (2015). Gbm: Generalized boosted regression models (R package version 2.1.1). https://cran.r-project.org/web/packages/gbm/index.html
  • Schwartz, Z., Uysal, M., Webb, T., & Altin, M. (2016). Hotel daily occupancy forecasting with competitive sets: a recursive algorithm. International Journal of Contemporary Hospitality Management, 28(2), 267–285. https://doi.org/10.1108/IJCHM-10-2014-0507
  • Schwartz, Z., Webb, T., van der Rest, J.-P. I., & Koupriouchina, L. (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. https://doi.org/10.1177/1354816619884800
  • Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In C. Cortes, N. Lawrence, D. Lee, M. Sugiyama & R. Garnett (Eds.), Advances in Neural Information Processing Systems (pp. 802–810).
  • Taieb, S. B., Bontempi, G., Atiya, A. F., & Sorjamaa, A. (2012). A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Systems with Applications, 39(8), 7067–7083. https://doi.org/10.1016/j.eswa.2012.01.039
  • Takens, F. (1981). Detecting strange attractors in turbulence. In D. Rand & L.-S. Young (Eds.), Dynamical Systems and Turbulence, Warwick 1980: Proceedings of a Symposium Held at the University of Warwick 1979/80 (pp. 366–381). Springer. https://doi.org/10.1007/BFb0091903
  • Talluri, K. T., & Van Ryzin, G. J. (2006). The theory and practice of revenue management (Vol. 68). Springer Science & Business Media.
  • Tse, T. S. M., & Poon, Y. T. (2015). Analyzing the use of an advance booking curve in forecasting hotel reservations. Journal of Travel & Tourism Marketing, 32(7), 852–869. https://doi.org/10.1080/10548408.2015.1063826
  • Vinod, B. (2004). Unlocking the value of revenue management in the hotel industry. Journal of Revenue and Pricing Management, 3(2), 178–190. https://doi.org/10.1057/palgrave.rpm.5170105
  • Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., & Fouilloy, A. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105(8), 569–582. https://doi.org/10.1016/j.renene.2016.12.095
  • Wang, X., Smith, K., & Hyndman, R. J. (2006). Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery, 13(3), 335–364. https://doi.org/10.1007/s10618-005-0039-x
  • Weatherford, L. R., & Belobaba, P. P. (2002). Revenue impacts of fare input and demand forecast accuracy in airline yield management. Journal of the Operational Research Society, 53(8), 811–821. https://doi.org/10.1057/palgrave.jors.2601357
  • Weatherford, L. R., & Kimes, S. E. (2003). A comparison of forecasting methods for hotel revenue management. International Journal of Forecasting, 19(3), 401–415. https://doi.org/10.1016/S0169-2070(02)00011-0
  • Wirtz, J., Kimes, S. E., Theng, J. H. P., & Patterson, P. (2003). Revenue management: Resolving potential customer conflicts. Journal of Revenue and Pricing Management, 2(3), 216–226. https://doi.org/10.1057/palgrave.rpm.5170068
  • Wright, M. N. (2021). Ranger: A fast implementation of random forests (R package version 0.13.1). https://cran.r-project.org/web/packages/ranger/index.html
  • Yüksel, S. (2007). An integrated forecasting approach to hotel demand. Mathematical and Computer Modelling, 46(7–8), 1063–1070. https://doi.org/10.1016/j.mcm.2007.03.008

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.