Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
International Journal of Data Science and Analytics

04.12.2018 | Applications

Forecasting hotel reservations with long short-term memory-based recurrent neural networks

verfasst von: Jian Wang, Amar Duggasani

Erschienen in: International Journal of Data Science and Analytics

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Hotel reservations tie up a much large portion of a hotels annual revenue. It is of great benefit for hotel managers to accurately forecast the numbers of reservations each day so that they can make better operational and tactical decisions. In this study, we review three types of forecasting methods commonly used in practice and briefly illustrate the concepts of neural networks. We then propose two long short-term memory (LSTM) models based on recurrent neural networks. Actual reservations of four hotels in the USA are used to estimate and test the proposed two models. To measure the relative performance, six machine learning (ML) models of decision tree, multilayer perceptron, lasso, linear regression, random forest, and ridge are also estimated and tested against the same datasets. The empirical results show that, on average, the forecasting accuracy of using LSTM models has been improved about 3.0% over that of using the best of the ML models.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Literatur
1.
Kimes, S.E.: Group forecasting accuracy in hotels. J. Oper. Res. Soc. 50, 1104–1110 (1999)MATHCrossRef
2.
Wang, J.: Optimizing hotel group room rates. Cornell Hosp. Rep. 16(16), 24–27 (2015)
3.
Queenan, C.C., Ferguson, M., Higbie, J., Kapoor, R.: A comparison of unconstraining methods to improve revenue management systems. Prod. Oper. Manag. 16(6), 729–746 (2007)CrossRef
4.
Weatherford, L.R., Polt, S.: Better unconstraining of airline demand data in revenue management systems for improved forecast accuracy and greater revenue. J. Revenue Pricing Manag. 1(3), 234–254 (2002)CrossRef
5.
Vinod, B.: Unlocking the value of revenue management in the hotel industry. J. Revenue Pricing Manag. 3, 178–190 (2004)CrossRef
6.
Ivanov, S., Zhechev, V.: Hotel revenue management a acritical literature review. Tourism 60(2), 175–197 (2012)
7.
Phumchusri, N., Mongkolkul, P.: Hotel room demand forecasting via observed reservation information, 1978–1985. In: Kachitvichyanukul, V., Luong, H., Pitakaso, R. (eds.) Proceedings of the Asia Pacific Industrial Engineering and Management Systems Conference (2012)
8.
Rajopadhye, M., Ghalia, M.B., Wang, P.P., Baker, T., Eister, C.: Forecasting uncertain hotel room demand. Inf. Sci. 132(1–4), 1–11 (2001)CrossRef
9.
Zakhary, A., El Gayar, N., Atyia, A.: A comparative study of the pickup method and its variations using a simulated reservation hotel data. ICGST Int. J. Artif. Intell. Mach. Learn. 8, 15–21 (2008). ICGST-AIML (Special issue on computational methods for the tourism industry)
10.
Zakhary, A., Atyia, A., Shishiny, H.El, Gayar, N.N.: Forecasting hotel arrivals and occupancy using Monte Carlo simulation. J. Revenue Pricing Manag. 10(4), 344–366 (2011)CrossRef
11.
Weatherford, L.R., Kimes, S.E.: A comparison of forecasting methods for hotel revenue management. Int. J. Forecast. 19, 401–415 (2003)CrossRef
12.
Pekgun, P., Menich, R.P., Acharya, S., Finch, P.G., Deschamps, F., Mallery, K., Sistine, J.V., Christianson, K., Fuller, J.: Carlson rezidor hotel group maximizes revenue through improved demand management and price optimization. Interfaces 43(1), 21–36 (2013)CrossRef
13.
Wang, J.: A realization of an apartment dynamic pricing system. J. Revenue Pricing Manag. 7, 256–265 (2008)CrossRef
14.
Yuksel, S.: An integrated forecasting approach to hotel demand. Math. Comput. Model. 46(7–8), 1063–1070 (2007)CrossRef
15.
Pan, B., Wu, C., Song, H.: Forecasting hotel demand using search engine data. J. Hosp. Tour. Technol. 3(3), 196–220 (2012)
16.
17.
Law, R., Au, N.: A neural network model to forecast japanese demand for travel to Hong Kong. Tour. Manag. 20, 89–97 (1999)CrossRef
18.
Palmer, A., Montano, J.J., Sese, A.: Designing an artificial neural network for forecasting tourism time series. Tour. Manag. 27, 781–790 (2006)CrossRef
19.
Corazza, M., Fasano, G., Mason, F.: An artificial neural network-based techniques for on-line hotel booking. Proc. Econ. Finance 15, 45–55 (2014)CrossRef
20.
Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. Preprint (2015). arXiv:​1506.​00019
21.
22.
Medsker, L., Jain, L.: Recurrent Neural Networks: Design and Applications. CRS Press, Boca Raton (2001)
23.
24.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
25.
Graves, A., Jaitly, N., Mohamed, A.R.: Hybrid speech recognition with deep bidirectional LSTM. In: IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 273–278, IEEE (2013)
26.
Malhotra, P., Vig, L., Shroff, G., Agarwal, P.: Long short term memory networks for anomaly detection in time series. In: Conference: 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2015, At Bruges (2015)
27.
Zhuge, Q., Xu, L., Zhang, G.: LSTM neural network with emotional analysis for prediction of stock price. Eng. Lett. 25(2), 167–175 (2017)
28.
Akita, R., Yoshihara, A., Matsubara, T., Uehara, K.: Deep learning for stock prediction using numerical and textual information. In: 2016 IEEE/ACIS 15th International Conference on Computer and Information Science, Okayama (2016)
29.
30.
Sak, H., Senior, A., Beaufays, F.: Long short-term memory recurrent neural network architectures for large scale acoustic modeling. In: INTERSPEECH (2014)
31.
Zheng, J., Xu, C., Zhang, Z., Li, X.: Electric load forecasting in smart grid using long-short-term-memory based recurrent neural network. In: The 51st Annual Conference on Information Systems and Sciences Proceedings, Baltimore (2017)
32.
Zaytar, M.A., Amrani, C.El: Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. Int. J. Comput. Appl. 143(11), 7–11 (2016)
33.
Ma, X., Tao, Z., Wang, Y., Yu, H., Wang, Y.: Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp. Res. C 54, 187–197 (2015)CrossRef
34.
Yenala, H., Jhanwar, A., Chinnakotla, M.K., Goyal, J.: Deep learning for detecting inappropriate content in text. Int. J. Data Sci. Anal. 6(4), 273–286 (2007)CrossRef
35.
Kim, J., Shim, K., Cao, L., Lee, J.G., Lin, X., Moon, Y.S.: Convolutional Bi-directional LSTM for detecting inappropriate query suggestions in web search. In: Kim, J., Shim, K., Cao, L., Lee, J.G., Lin, X., Moon Y.S. (eds.) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science, vol 10234. Springer, Cham (2017)
36.
Duan, Y., Lv, Y., Wang, F.Y.: Travel time prediction with LSTM neural network. In: 2016 IEEE 19th International Conference on Intelligent Transportation System (ITSC), Rio de Janeiro (2016)
37.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, H., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)MathSciNetMATH
38.
39.
Wasserman, P.D.: Neural Computing: Theory and Practice. Van Nostrand Reinhold, New York (1989)
40.
Kaastra, I., Boyd, M.: Designing a neural network for forecasting financial and economic time series. Neurocomputing 10, 215–236 (1996)CrossRef
41.
Pascanu, R., Mikolov, T., Bengio, Y.: On the difficulty of training recurrent neural networks. ICML 3(28), 1310–1318 (2013)
42.
Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157–166 (1994)CrossRef
43.
Lee, C.K., Lin, T.D., Lin, C.H.: Pattern analysis on the booking curve of an inter-city railway. J. East. Asia Soc. Transp. Stud. 6, 303–317 (2005)
44.
Tse, T.S.M., Poon, Y.T.: Analyzing the use of an advance booking curve in forecasting hotel reservations. J. Travel Tour. Mark. 32, 852–869 (2015)CrossRef
45.
Shaw, J.: Neural network resource guide. AI Expert 8(2), 48–54 (1992)
46.
Law, R.: Back-propagation learning in improving the accuracy of neural network-based tourism demand forecasting. Tour. Manag. 21(4), 331–340 (2000)CrossRef
Metadaten
Titel
Forecasting hotel reservations with long short-term memory-based recurrent neural networks
verfasst von
Jian Wang
Amar Duggasani
Publikationsdatum
04.12.2018
Verlag
Springer International Publishing
Erschienen in
International Journal of Data Science and Analytics
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-018-0162-6

Premium Partner

    Bildnachweise