Psillakis, Zaharias and Panagopoulos, Alkiviadis and Kanellopoulos, Dimitris (2008): Low Cost Inferential Forecasting and Tourism Demand in Accommodation Industry. Published in: TOURISMOS: An International Multidisciplinary Journal of Tourism , Vol. 4, No. 2 (15. April 2009): pp. 47-68.
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This paper establishes a low cost inferential model that allows reliable time series forecasts. The model provides a naive unique computationally straightforward approach based on widely-used additive models. It refers to the decomposition of every time series value in “random” components, which are compounded to constitute a “Fibonacci type” predictor random variable. The expected value of this predictor gives a forecast of a future time series value. The standard deviation of the predictor serves to construct a prediction interval at a predefined confidence level. The major features of our model are: forecasting accuracy, simplicity of the implementation technique, generic usefulness, and extremely low cost effort. These features enable our model to be adopted by tourism practitioners on various types of forecasting demands. In this paper, we present an application study to forecast tourism demand that exists in the Greek accommodation industry (i.e. in Greece and in the broad region of Athens). In the application study, two independent approaches have been adopted. In the first approach we implemented our model, and in the second approach we implemented the well-known Box-Jenkins method.
|Item Type:||MPRA Paper|
|Original Title:||Low Cost Inferential Forecasting and Tourism Demand in Accommodation Industry|
|Keywords:||Time series, forecasting models, Naïve models, ex-post and ex-ante forecasts, forecast accuracy and validation, tourism demand|
|Subjects:||O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development
M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M1 - Business Administration
L - Industrial Organization > L8 - Industry Studies: Services > L83 - Sports ; Gambling ; Restaurants ; Recreation ; Tourism
|Depositing User:||Evangelos Christou|
|Date Deposited:||23. Sep 2010 13:31|
|Last Modified:||12. May 2015 01:46|
Box, G.E.P. & Jenkins, G.M. (1994). Time Series Analysis: Forecasting and Control, (3rd ed.). New Jersey, Prentice Hall.
Burger, C.J.S.C., Dohnal, M., Kathrada, M. & Law, R. (2001). A practitioner guide to time-series methods for tourism demand forecasting-a case study of Durban, South Africa. Tourism Management, Vol. 22, pp.403-409.
Chan, Y. M., Hui, T. K. & Yuen, E. (1999). Modelling the impact of sudden environmental changes on visitor arrival forecasts: the case of the Gulf war. Journal of Travel Research, Vol. 37, No.4, pp.391-394.
Chu, F. (2004) Forecasting tourism demand: a cubic polynomial approach. Tourism Management, Vol. 25, pp.209-218.
Dharmaratne, G.S. (1995). Forecasting tourist arrivals. Annals of Tourism Research, Vol. 22, No.4, pp.804-818.
Franses, P.H. (2004). Time series models for business and economic forecasting. Cambridge, University Press.
Frechtling, D.C. (2001). Forecasting tourism demand: methods and strategies. Oxford, Butterworth Heinemann.
Frees, E.W. (1996). Data Analysis Using Regression Models - The Business Perspective. New York, Prentice Hall. GNTO: Greek National Tourism Organization. Annual Report 1990-1999.
Louvieris, P. (2002). Forecasting international tourism demand for Greece: A contingency approach (using Cyberfilter and ARIMA model). Journal of Travel and Tourism marketing, Vol. 13, pp.21-40.
Makri, F.S. & Psillakis, Z.M. (1997). Bounds for reliability of k-within connected–(r,s)-out-of-(m,n) failure systems. Microelectronics and Reliability, Vol. 37, No.8, pp.1217-1224.
Makridakis, S. & Hibon, M. (1979). Accuracy of forecasting: An empirical investigation. Journal of the Royal Statistical Society A, Vol.142, pp.97-145.
Mendenhall, W. & Sincich, T. (1996). A Second course in Statistics – Regression Analysis (5th ed.). New Jersey, Prentice Hall.
Minitab Release 14. (2002). Statistical software for windows.
Panagopoulos, A.A. (2005). A statistical model of tourism (development) in Greece within the framework of European Union (1990-1999): the seasonality effect. Unpublished Phd Dissertation (in Greek), University of Piraeus, Greece.
Panagopoulos, A., Psillakis, Z. & Kanellopoulos, D. (2004). A low cost reliable forecasting model of tourism data. In F.D. Pineda & C.A. Brebbia (Eds). Sustainable Tourism (pp.343-352), Southampton: WIT Press.
Ross, S. (1998). A first course in probability. New Jersey, Prentice Hall.
Smith, S.J. (1995). Tourism analysis: A handbook. London, Longman.
Song, H. & Witt, S.F. (2006). Forecasting international tourism flows to Macau. Tourism Management, Vol. 27, No.2, pp.214-224.
Turner, L.W., Kulendran, N. & Pergat, V. (1995). Forecasting New Zealand tourism demand with aggregated data. Tourism Economics, Vol. 1, No.1, pp.51-69.
Witt, C.A., Witt, S. F. & Wilson, N. (1994). Forecasting international tourist flows. Annals of Tourism Research, Vol.21, No.3, pp.612-628.
Witt, S.F., Song, H. & Louvieris, P. (2003). Statistical testing in forecasting model selection. Journal of Travel Research, Vol. 42, pp.151-158.