Data-Driven Hospitality: Advanced Forecasting Models for Hotel Occupancy
-
Yerik Afrianto Singgalen
*
Atma Jaya Catholic University of Indonesia, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
Accurate forecasting of hotel booking demand is essential for resource optimization, revenue maximization, and enhanced customer experience in the hospitality industry. This study evaluates the performance of three forecasting models, ARIMA, Prophet, and LSTM, using historical booking data to identify the most effective approach for predicting demand. The evaluation employed four key metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R²), providing a comprehensive comparison. The results indicate that the LSTM model outperformed the others in prediction accuracy, achieving the lowest MAE (2.71) and MAPE (21.33%), demonstrating its strength in capturing complex patterns. However, its negative R-squared value (-0.20) suggests limitations in explaining overall data variance compared to ARIMA (0.51) and Prophet (0.50). The Prophet model excelled in seasonal decomposition but showed the highest MAPE (71.86%), while ARIMA delivered robust residual diagnostics, adhering well to model assumptions with consistent variance and randomness in residuals. The findings suggest that while LSTM is most effective for short-term forecasting, ARIMA and Prophet provide better interpretability and reliability for long-term trend analysis. A hybrid approach combining the strengths of all three models is recommended to enhance predictive accuracy and robustness. This study provides actionable insights for industry stakeholders seeking to improve decision-making processes and operational efficiency through advanced forecasting techniques.
Downloads
References
A. Ampountolas and M. Legg, “Predicting daily hotel occupancy: a practical application for independent hotels,” J. Revenue Pricing Manag., vol. 23, no. 3, pp. 197–205, 2024, doi: 10.1057/s41272-023-00445-7.
M. Y. Anshori et al., “Optimization of Hotel W Management through Performance Comparison of Support Vector Machine and Linear Regression Algorithm in Forecasting Occupancy,” Nonlinear Dyn. Syst. Theory, vol. 24, no. 3, pp. 228–235, 2024, [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195696703&origin=inward
A. Ampountolas, “Addressing complex seasonal patterns in hotel forecasting: a comparative study,” J. Revenue Pricing Manag., 2024, doi: 10.1057/s41272-024-00494-6.
M. Y. Anshori et al., “Optimization of Hotel Y Management through Application of Occupancy Forecasting by Support Vector Machine and K-Nearest Neighbors Methods,” Nonlinear Dyn. Syst. Theory, vol. 24, no. 4, pp. 331–339, 2024, [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85200775394&origin=inward
L. Zhang, Z. Huang, Z. Wang, X. Li, and F. Sun, “An urban charging load forecasting model based on trip chain model for private passenger electric vehicles: A case study in Beijing,” Energy, vol. 299, 2024, doi: 10.1016/j.energy.2024.130844.
J. Xu, W. Zhang, H. Li, X. (Kevin) Zheng, and J. Zhang, “User-generated photos in hotel demand forecasting,” Ann. Tour. Res., vol. 108, 2024, doi: 10.1016/j.annals.2024.103820.
R. G. Talele and N. Kharate, “Predictive modeling for urban air quality: A machine learning comparison study,” AIP Conference Proceedings, vol. 3156, no. 1. 2024. doi: 10.1063/5.0227587.
V. Z. Mohale and I. C. Obagbuwa, “Assessing the impact of climate variability on wheat yield in Bloemfontein wheat farms through time series analysis,” Edelweiss Appl. Sci. Technol., vol. 8, no. 5, pp. 1213–1234, 2024, doi: 10.55214/25768484.v8i5.1826.
S. Pargaien, R. Prakash, V. P. Dubey, and D. Singh, “Crop cover identification based on different vegetation indices by using machine learning algorithms,” Environ. Res. Technol., vol. 7, no. 3, pp. 422–434, 2024, doi: 10.35208/ert.1446909.
X. Zhang, P. Li, X. Han, Y. Yang, and Y. Cui, “Enhancing Time Series Product Demand Forecasting with Hybrid Attention-Based Deep Learning Models,” IEEE Access, vol. 12, pp. 190079–190091, 2024, doi: 10.1109/ACCESS.2024.3516697.
A. Perumal, C. Mazumdar, T. N. Srinatha, S. Rath, and S. Likhitha, “Mitigating Climate Impact: A Machine Learning Approach to Forecast Methane Emissions from Indian Livestock,” Indian J. Agric. Econ., vol. 79, no. 3, pp. 610–620, 2024, doi: 10.63040/25827510.2024.03.020.
Z. Wang et al., “Time series models in prediction of severe fever with thrombocytopenia syndrome cases in Shandong province, China,” Infect. Dis. Model., vol. 9, no. 1, pp. 224–233, 2024, doi: 10.1016/j.idm.2024.01.003.
N. B. Korade and M. Zuber, “Forecasting Stock Price Using Time-Series Analysis and Deep Learning Techniques,” Lecture Notes in Electrical Engineering, vol. 1146 LNEE. pp. 403–421, 2024. doi: 10.1007/978-981-97-0037-0_31.
S. A. Cancemi, M. Angelucci, R. Lo Frano, and S. Paci, “Surrogate Modeling Advancements in Nuclear Fuel Engineering: Bridging the Gap Between Accuracy and Efficiency,” Proceedings of 2024 31st International Conference on Nuclear Engineering, ICONE 2024, vol. 11. 2024. doi: 10.1115/ICONE31-135798.
C. Pires and M. V. Martins, “Enhancing Water Management: A Comparative Analysis of Time Series Prediction Models for Distributed Water Flow in Supply Networks,” Water (Switzerland), vol. 16, no. 13, 2024, doi: 10.3390/w16131827.
K. Li and Y. Zhou, “Improved Financial Predicting Method Based on Time Series Long Short-Term Memory Algorithm,” Mathematics, vol. 12, no. 7, 2024, doi: 10.3390/math12071074.
N. Tripathy, P. Satapathy, S. Hota, S. K. Nayak, and D. Mishra, “Empirical Forecasting Analysis of Bitcoin Prices: A Comparison of Machine Learning, Deep Learning, and Ensemble Learning Models,” Int. J. Electr. Comput. Eng. Syst., vol. 15, no. 1, pp. 21–29, 2024, doi: 10.32985/ijeces.15.1.3.
F. Meng and R. Dou, “Prophet-LSTM-BP Ensemble Carbon Trading Price Prediction Model,” Comput. Econ., vol. 63, no. 5, pp. 1805–1825, 2024, doi: 10.1007/s10614-023-10384-5.
S. A. Wagle, R. Harikrishnan, and K. Kotecha, “Bilinear LSTM with Bayesian Gaussian Optimization for Predicting Tomato Plant Disease Using Meteorological Parameters,” Ing. des Syst. d’Information, vol. 29, no. 2, pp. 479–492, 2024, doi: 10.18280/isi.290209.
M. Katsouda, K. Kollias, C. Halkiopoulos, and B. Boutsinas, “Mining Association of Outliers in Time Series,” Springer Proceedings in Business and Economics, vol. 7. pp. 433–444, 2024. doi: 10.1007/978-3-031-54342-5_26.
F. Binesh, A. M. Belarmino, J. P. van der Rest, A. K. Singh, and C. Raab, “Forecasting hotel room prices when entering turbulent times: a game-theoretic artificial neural network model,” Int. J. Contemp. Hosp. Manag., vol. 36, no. 4, pp. 1044–1065, Jan. 2024, doi: 10.1108/IJCHM-10-2022-1233.
D. Contessi, L. Viverit, L. N. Pereira, and C. Y. Heo, “Decoding the future: Proposing an interpretable machine learning model for hotel occupancy forecasting using principal component analysis,” Int. J. Hosp. Manag., vol. 121, 2024, doi: 10.1016/j.ijhm.2024.103802.
P. Kumar and P. Ekka, “Statistical analysis of the impacts of COVID-19 pandemic on the small and large-scale tourism sectors in developing countries,” Environ. Dev. Sustain., vol. 26, no. 4, pp. 9625–9659, 2024, doi: 10.1007/s10668-023-03112-4.
C. Song et al., “A novel deep-learning framework for short-term prediction of cooling load in public buildings,” J. Clean. Prod., vol. 434, 2024, doi: 10.1016/j.jclepro.2023.139796.
S. Jain, S. Agrawal, E. Mohapatra, and K. Srinivasan, “A novel ensemble ARIMA-LSTM approach for evaluating COVID-19 cases and future outbreak preparedness,” Heal. Care Sci., vol. 3, no. 6, pp. 409–425, 2024, doi: 10.1002/hcs2.123.
Z. Huang, J. Lin, L. Lin, J. Chen, L. Zheng, and K. Zhang, “A Sensitive LSTM Model for High Accuracy Zero-inflated Time-series Prediction,” IEEE Access, vol. 12, pp. 171527–171539, 2024, doi: 10.1109/ACCESS.2024.3498933.
J. Toleti, S. Singh, and R. P. Kumar, “Developing Robust Crypto-Currency Trading Strategies: A Fusion of Machine Learning and Hidden Markov Modelling for Enhanced Performance,” Proceedings of CONECCT 2024 - 10th IEEE International Conference on Electronics, Computing and Communication Technologies. 2024. doi: 10.1109/CONECCT62155.2024.10677046.
T. J. Omotosho, “Oil Production Prediction Using Time Series Forecasting and Machine Learning Techniques,” Society of Petroleum Engineers - SPE Nigeria Annual International Conference and Exhibition, NAIC 2024. 2024. doi: 10.2118/221728-MS.
Y. Peng, H. Su, M. Sun, and M. Li, “Reconstructing historical forest fire risk in the non-satellite era using the improved forest fire danger index and long short-term memory deep learning-a case study in Sichuan Province, southwestern China,” For. Ecosyst., vol. 11, 2024, doi: 10.1016/j.fecs.2024.100170.
E. V. de Oliveira, D. P. Aragão, and L. M. G. Gonçalves, “A New Auto-Regressive Multi-Variable Modified Auto-Encoder for Multivariate Time-Series Prediction: A Case Study with Application to COVID-19 Pandemics,” Int. J. Environ. Res. Public Health, vol. 21, no. 4, 2024, doi: 10.3390/ijerph21040497.
D. Zhang and B. Niu, “Leveraging online reviews for hotel demand forecasting: A deep learning approach,” Inf. Process. Manag., vol. 61, no. 1, 2024, doi: 10.1016/j.ipm.2023.103527.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Data-Driven Hospitality: Advanced Forecasting Models for Hotel Occupancy
Pages: 2709-2722
Copyright (c) 2025 Yerik Afrianto Singgalen
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
