Developing a hybrid forecasting system for international hotel revenue : The case of Taiwan
*Yi-Hui LiangCorresponding authorgerman@isu.edu.twDepartment of Information Management1, Section 1, Hsueh-Chen RoadI-SHOU UniversityTa-Hsu Hsiang, Kaohsiung County, Taiwan (R.O.C.)0000-0001-9440-8274View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 10 Dec 2024
- Published Online:
- 16 Jan 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2050
- Pages:
- 715–726
Abstract
Keywords
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References
[1] L. Koupriouchina, P. van der Rest, J., and Z. Schwartz. On revenue management and the use of occupancy forecasting error measures. International Journal of Hospitality Management, vol. 41, pp. 104-114 (2014).
[2] G. Cetin, T. Demirçiftçi, and A. Bilgihan. Meeting revenue management challenges: Knowledge, skills and abilities. International Journal of Hospitality Management, vol. 57, pp. 132-142 (2016).
[3] R. Law. Room occupancy rate forecasting: a neural network approach. International Journal of Contemporary Hospitality Management, vol. 10, no. 6, pp. 234-239 (1998).
[4] D. Barman, R. Sarkar, A. Tudu, and N. Chowdhury. Personalized query recommendation system: A genetic algorithm approach. Journal of Interdisciplinary Mathematics, vol. 23, no. 2, pp. 523-535 (2020).
[5] V. Kapoor, and S. Dey. A genetic algorithm based decision support system for forecasting security prices in stock index. Journal of Information and Optimization Sciences, vol. 43, no. 8, pp. 2153–2166 (2022).
[6] N. S. Sukanya and P. R. Thangaiah. An integrated cuckoo search-genetic algorithm for mining frequent itemsets. Journal of Discrete Mathematical Sciences and Cryptography, vol. 25, no. 3, pp. 671-690 (2022).
[7] J. A. Bullinaria. Using evolution to improve neural network learning: pitfalls and solutions. Neural Computing and Applications, vol. 16, no. 3, pp. 209-226 (2007).
[8] G. Zhang, J. Wu, B. Pan, J. Li, M. Ma, M. Zhang, and J. Wang. Improving daily occupancy forecasting accuracy for hotels based on EEMD-ARIMA model. Tourism Economics, vol. 23, no. 7, pp. 1496-1514 (2017).
[9] K. Jeong, C. Koo, and T. Hong. An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network). Energy, vol. 71, pp. 71-79 (2014).
[10] R. G. Cross, J. A. Higbie, and Z. N. Cross. Milestones in the application of analytical pricing and revenue management, Journal of Revenue and Pricing Management, vol. 10, no. 1, pp. 8-18 (2011).
[11] K. Talluri, and G. Van Ryzin. Revenue management under a general discrete choice model of consumer behavior. Management Science, vol. 50, no. 1, pp. 15-33 (2004).
[12] D. C. Wu, H. Song, and S. Shen. New developments in tourism and hotel demand modeling and forecasting. International Journal of Contemporary Hospitality Management, vol. 29, no. 1, pp. 507-529 (2017).
[13] Z. Schwartz, M. Uysal, T. Webb, and M. Altin. Hotel daily occupancy forecasting with competitive sets: a recursive algorithm. International Journal of Contemporary Hospitality Management, vol. 28, no. 2, pp. 267-285 (2016).
[14] K. Kaya, Y. Yılmaz, Y. Yaslan, Ş. G. Öğüdücü, and F. Çıngı. Demand forecasting model using hotel clustering findings for hospitality industry. Information Processing & Management, vol. 59, no. 1, pp. 102816 (2022).
[15] X. Xu, G. Xiao, and D. Gursoy. Maximizing profits through optimal pricing and sustainability strategies: A joint optimization approach. Journal of Hospitality Marketing & Management, vol. 26, no. 4, pp. 395-415 (2017).
[16] L. N. Pereira. An introduction to helpful forecasting methods for hotel revenue management. International Journal of Hospitality Management, vol. 58, pp. 13-23 (2016).
[17] N. Oses, J. K. Gerrikagoitia, and A. Alzua. Modelling and prediction of a destination’s monthly average daily rate and occupancy rate based on hotel room prices offered online. Tourism Economics, vol. 22, no. 6, pp. 1380-1403 (2016).
[18] A. Haensel, and G. Koole. Booking horizon forecasting with dynamic updating: A case study of hotel reservation data. International Journal of Forecasting, vol. 27, no. 3, pp. 942-960 (2011).
[19] A. Zakhary, A. F. Atiya., H. El-Shishiny, and N. E. Gayar. Forecasting hotel arrivals and occupancy using Monte Carlo simulation. Journal of Revenue and Pricing Management, vol. 10, pp. 344-366 (2011).
[20] J. Guadix, P. Cortés, L. Onieva, and J. Muñuzuri. Technology revenue management system for customer groups in hotels. Journal of Business Research, vol. 63, no. 5, pp. 519-527 (2010).
[21] W. M. Lim. Alternative models framing UK independent hoteliers’ adoption of technology. International Journal of Contemporary Hospitality Management, vol. 21, no. 5, pp. 610-618 (2009).
[22] S. Yüksel. An integrated forecasting approach to hotel demand. Mathematical and Computer Modelling, vol. 46, no. 7-8, pp. 1063-1070 (2007).
[23] L. R. Weatherford, S. E. Kimes, and D. A. Scott, D. A. Forecasting for hotel revenue management: Testing aggregation against disaggregation. The Cornell Hotel and Restaurant Administration Quarterly, vol. 42, no. 4, pp. 53-64 (2001).
[24] R. Law. Demand for hotel spending by visitors to Hong Kong: A study of various forecasting techniques. Journal of Hospitality & Leisure Marketing, vol. 6, no. 4, pp. 17-29 (1999).
[25] L. R. Weatherford, S. E. Kimes, and D. A. Scott. Forecasting for hotel revenue management: Testing aggregation against disaggregation. Cornell hotel and restaurant administration quarterly, vol. 42, no. 4, 53-64 (2001).




