Enhancing the accuracy of short-term forecasts for cancellation rates offers revenue managers the opportunity to formulate a pricing strategy for the upcoming day, yielding favorable economic outcomes. This study proposes a methodology based on dynamic models utilizing machine learning methods such as LightGBM, XGBoost, Random Forest, and ANN-MLP, highlighting the importance of data dimensionality reduction while using higher-performing variables to improve predictive accuracy and stability. Notably, lagged variables within a few days of the forecasted date and reservations made through OTAs and BAR-rated reservations exhibit significant predictive power. The results indicate that artificial neural network multi-layer perceptron (ANN-MLP) outperforms other models, especially in longer forecast horizons. The study recommends adaptable strategies considering historical data and temporal trends and leveraging ANN-MLP for superior accuracy. The findings offer valuable insights for industry practitioners, providing a nuanced understanding of cancellation patterns and suggesting strategies to optimize cancellation prediction models in a competitive marketplace.
AfifahKYulitaINSarathanI (2021) Sentiment analysis on telemedicine app reviews using xgboost classifier. In: 2021 International Conference on Artificial Intelligence and Big Data Analytics, Bandung, Indonesia, 27-29 October 2021, pp. 22–27, IEEE.
2.
AmpountolasA (2019) Forecasting hotel demand uncertainty using time series bayesian var models. Tourism Economics25(5): 734–756.
3.
AmpountolasA (2021) Modeling and forecasting daily hotel demand: a comparison based on sarimax, neural networks, and garch models. Forecasting3(3): 580–595.
4.
AmpountolasA (2024) Addressing complex seasonal patterns in hotel forecasting: a comparative study. Journal of Revenue and Pricing Management1–10.
5.
AmpountolasALeggMP (2021) A segmented machine learning modeling approach of social media for predicting occupancy. International Journal of Contemporary Hospitality Management33(6): 2001–2021.
6.
AmpountolasALeggM (2023) Predicting daily hotel occupancy: a practical application for independent hotels. Journal of Revenue and Pricing Management23: 197–205.
7.
AntònioNAlmeidaANunesL (2017) Predicting hotel bookings cancellation with a machine learning classification model.
8.
AntonioNAlmeidaAdNunesL (2017) Predicting hotel booking cancellations to decrease uncertainty and increase revenue. Tourism & Management Studies13(2): 25–39.
9.
AntónioNAlmeidaAdNunesL (2019) Predictive models for hotel booking cancellation: a semi-automated analysis of the literature. Tourism & Management Studies15(1): 7–21.
10.
BelkinMHsuDMaS, et al. (2019) Reconciling modern machine-learning practice and the classical bias-variance trade-off. Proceedings of the National Academy of Sciences116(32): 15849–15854.
11.
BojerCS (2022) Understanding machine learning-based forecasting methods: a decomposition framework and research opportunities. International Journal of Forecasting38(4): 1555–1561.
12.
BreimanL (2001) Random forests. Machine Learning45(1): 5–32.
13.
ChatterjeeH (2001) Forecasting for cancellations. AGIFORS Reservations and Yield Management Study Group, Bangkok, Thailand.
14.
ChenTGuestrinC (2016a) Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, 13–17 August, 2016, pp. 785–794.
15.
ChenTGuestrinC (2016b). Xgboost: a scalable tree boosting system. CoRR abs/1603.02754.
16.
ChenSNgaiEWKuY, et al. (2023) Prediction of hotel booking cancellations: integration of machine learning and probability model based on interpretable feature interaction. Decision Support Systems170: 113959.
17.
DickeyDAFullerWA (1979) Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association74(366a): 427–431.
18.
DieboldFXMarianoRS (1995) Comparing predictive accuracy. Journal of Business & Economic Statistics13(3): 253–263.
19.
DongQGongSZhuX (2018) Imbalanced deep learning by minority class incremental rectification. IEEE Transactions on Pattern Analysis and Machine Intelligence41(6): 1367–1381.
20.
FalkMVieruM (2018) Modelling the cancellation behaviour of hotel guests. International Journal of Contemporary Hospitality Management30(10): 3100–3116.
21.
FriedmanJHastieTTibshiraniR, et al. (2001) The Elements of Statistical Learning, Volume 1. New York: Springer series in statistics.
HastieTTibshiraniRFriedmanJ (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer Science & Business Media.
24.
HaynesNEganD (2020) The perceptions of frontline employees towards hotel overbooking practices: exploring ethical challenges. Journal of Revenue and Pricing Management19: 119–128.
25.
IliescuDCGarrowLAParkerRA (2008) A hazard model of us airline passengers’ refund and exchange behavior. Transportation Research Part B: Methodological42(3): 229–242.
26.
Joseph BeaulieuJMironJA (1993) Seasonal unit roots in aggregate us data. Journal of Econometrics55(1-2): 305–328.
27.
KeGMengQFinleyT, et al. (2017) Lightgbm: a highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems30.
28.
KimEJKimELKimM, et al. (2023) Post-pandemic hotel cancellation policy: situational cues as perceived risk triggers. Journal of Hospitality and Tourism Management55: 153–160.
29.
LawR (1998) Room occupancy rate forecasting: a neural network approach. International Journal of Contemporary Hospitality Management10(6): 234–239.
30.
LiuZJiangPWangJ, et al. (2023) Hospitality order cancellation prediction from a profit-driven perspective. International Journal of Contemporary Hospitality Management35(6): 2084–2112.
31.
ParkYHoJC (2019) Tackling overfitting in boosting for noisy healthcare data. IEEE Transactions on Knowledge and Data Engineering33(7): 2995–3006.
32.
PereiraLN (2016) An introduction to helpful forecasting methods for hotel revenue management. International Journal of Hospitality Management58: 13–23.
33.
PhillipsPCPerronP (1988) Testing for a unit root in time series regression. Biometrika75(2): 335–346.
34.
RajopadhyeMBen GhaliaMWangPP, et al. (2001) Forecasting uncertain hotel room demand. Information Sciences132(1-4): 1–11.
35.
Romero MoralesDWangJ (2010) Forecasting cancellation rates for services booking revenue management using data mining. European Journal of Operational Research202(2): 554–562.
36.
SánchezECSánchez-MedinaAJPellejeroM (2020) Identifying critical hotel cancellations using artificial intelligence. Tourism Management Perspectives35: 100718.
37.
Sánchez-MedinaAJC-SánchezE (2020) Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management89: 102546.
38.
SaputroPHNanangH (2021) Exploratory data analysis & booking cancelation prediction on hotel booking demands datasets. Journal of Applied Data Sciences2(1): 40–56.
39.
SchwartzZWebbTvan der RestJ-PI, et al. (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 Economics27(2): 273–291.
40.
SieragDDKooleGvan der MeiRD, et al. (2015) Revenue management under customer choice behaviour with cancellations and overbooking. European Journal of Operational Research246(1): 170–185.
41.
TalluriKTVan RyzinGVan RyzinG (2004) The Theory and Practice of Revenue Management. New York: Springer, Volume 1.
VinodB (2004) Unlocking the value of revenue management in the hotel industry. Journal of Revenue and Pricing Management3: 178–190.
44.
WebbTLeeMSchwartzZ, et al. (2023) Beyond accuracy: the advantages of the k-nearest neighbor algorithm for hotel revenue management forecasting. Tourism Economics30(5): 1216.
45.
WebRezPro (2024, 2). Hospitality by the Numbers: 40+ Stats You Should Know. Calgary, Alberta: WebRezPro.
46.
WuDCSongHShenS (2017) New developments in tourism and hotel demand modeling and forecasting. International Journal of Contemporary Hospitality Management29(1): 507–529.
47.
WuDCZhongSQiuRT, et al. (2022) Are customer reviews just reviews? hotel forecasting using sentiment analysis. Tourism Economics28(3): 795–816.
48.
ZakharyAAtiyaAFEl-ShishinyH, et al. (2011) Forecasting hotel arrivals and occupancy using Monte Carlo simulation. Journal of Revenue and Pricing Management10: 344–366.
49.
ZhangGPatuwoBEHuMY (1998) Forecasting with artificial neural networks: the state of the art. International Journal of Forecasting14(1): 35–62.
50.
ZhangGWuJPanB, et al. (2017) Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model. Tourism Economics23(7): 1496–1514.
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