Access the full text.
Sign up today, get DeepDyve free for 14 days.
J. Armstrong, Ed Scott, Armstrong Kluwer (2009)
Extrapolation for Time-Series and Cross-Sectional Data
Mihir Rajopadhye, M. Ghalia, Paul Wang, T. Baker, C. Eister (1999)
Forecasting uncertain hotel room demandProceedings of the 1999 American Control Conference (Cat. No. 99CH36251), 3
E. L’heureux (1986)
A NEW TWIST IN FORECASTING SHORT-TERM PASSENGER PICKUP
G. Allen, R. Fildes
Econometric forecasting
J. Armstrong (2009)
Selecting Forecasting Methods
L. Weatherford, S. Kimes (2003)
A comparison of forecasting methods for hotel revenue managementInternational Journal of Forecasting, 19
D. Wittink, Trond Bergestuen (2001)
Forecasting with Conjoint Analysis
Stanislav Ivanov, V. Zhechev (2011)
Hotel Revenue Management – A Critical Literature ReviewRevenue & Yield Management eJournal
K. Talluri, G. Ryzin (2004)
The Theory and Practice of Revenue Management
Z. Schwartz, Eli Cohen (2004)
Hotel Revenue-management ForecastingCornell Hotel and Restaurant Administration Quarterly, 45
Spyros Makridakis, A. Andersen, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler (1982)
The accuracy of extrapolation (time series) methods: Results of a forecasting competitionJournal of Forecasting, 1
Z. Schwartz, S. Hiemstra (1997)
Improving the Accuracy of Hotel Reservations Forecasting: Curves Similarity ApproachJournal of Travel Research, 36
C. Lim, Chia‐Lin Chang, M. McAleer (2009)
Forecasting h(m)otel guest nights in New ZealandInternational Journal of Hospitality Management, 28
C.J.S.C. Burger, M. Dohnal, M. Kathrada, R. Law (2001)
A practitioners guide to time-series methods for tourism demand forecasting - a case study of Durban, South AfricaTourism Management, 22
T. Tse, Y. Poon (2012)
Revenue management: resolving a revenue optimization paradoxInternational Journal of Contemporary Hospitality Management, 24
Hyunyoung Choi, H. Varian (2009)
Predicting the Present with Google TrendsMacroeconomics: Employment
Christopher Chen, S. Kachani (2007)
Forecasting and optimisation for hotel revenue managementJournal of Revenue and Pricing Management, 6
L. Weatherford, S. Kimes, D. Scott (2001)
Forecasting for Hotel Revenue Management: Testing Aggregation Against DisaggregationCornell Hotel and Restaurant Administration Quarterly, 42
Spyros Makridakis, M. Hibon (2000)
The M3-Competition: results, conclusions and implicationsInternational Journal of Forecasting, 16
J.S. Armstrong, R. Brodie, A. Parsons
Hypotheses in marketing science: literature review and publication audit
A. Lee (1990)
Airline reservations forecasting--probabilistic and statistical models of the booking process
G. Duncan, W. Gorr, J. Szczypula (2001)
Forecasting Analogous Time Series
N. Gayar, M. Saleh, A. Atiya, H. El-Shishiny, A. Zakhary, Heba Habib (2011)
An integrated framework for advanced hotel revenue managementInternational Journal of Contemporary Hospitality Management, 23
J. Armstrong (1982)
Strategies for Implementing Change: An Experiential ApproachGroup & Organization Management, 7
Sunil Gupta, P. Wilton (1987)
Combination of Forecasts: An ExtensionManagement Science, 33
S. Kimes (1999)
Group forecasting accuracy in hotelsJournal of the Operational Research Society, 50
D. Cranage, William Andrew (1992)
A comparison of time series and econometric models for forecasting restaurant salesInternational Journal of Hospitality Management, 11
J. Armstrong (2001)
Standards and Practices for ForecastingSocial Science Research Network
B. Pan, D. Wu, Haiyan Song (2012)
Forecasting hotel room demand using search engine data.Journal of Hospitality and Tourism Technology, 3
G. Erickson (1987)
Marketing managers need more than forecasting accuracyInternational Journal of Forecasting, 3
D. Cranage (2003)
Practical time series forecasting for the hospitality managerInternational Journal of Contemporary Hospitality Management, 15
J. Armstrong, J. Wiley, Sons, N. York, Chichester Brisbane, Toronto Singapore, Jon Armstrong, Scott, Xerox Kodak, He (1981)
Long-Range Forecasting: From Crystal Ball to Computer
R. Schmidgall, A. DeFranco (1998)
Budgeting and Forecasting: Current Practice in the Lodging IndustryCornell Hotel and Restaurant Administration Quarterly, 39
Purpose – The aim of this paper is to assess the performance of different widely‐adopted models to forecast Italian hotel occupancy. In particular, the paper tests the different models for forecasting the demand in hotels located in urban areas, which typically experience both business and leisure demand, and whose demand is often affected by the presence of special events in the hotels themselves, or in their neighborhood. Design/methodology/approach – Several forecasting models that the literature reports as most suitable for hotel room occupancy data were selected. Historical data on occupancy in five Italian hotels were divided into a training set and a test set. The parameters of the models were trained and fine‐tuned on the training data, obtaining one specific set for each of the five Italian hotels considered. For each hotel, each method, with corresponding best parameter choice, is used to forecast room occupancy in the test set. Findings – In the particular Italian market, models based on booking information outperform historical ones: pick‐up models achieve the best results but forecasts are in any case rather poor. Research limitations/implications – The main conclusions of the analysis are that the pick‐up models are the most promising ones. Nonetheless, none of the traditional forecasting models tested appears satisfactory in the Italian framework, although the data collected by the front offices can be rather rich. Originality/value – From a managerial point‐of‐view, the outcome of the study shows that traditional forecasting models can be considered only as a sort of “first aid” for revenue management decisions.
International Journal of Contemporary Hospitality Management – Emerald Publishing
Published: Apr 8, 2014
Keywords: Forecasting; Italy; Revenue management; City hotels; Hotel management
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.