STATISTICAL ANALYSIS OF DEMAND FOR TELECOMMUNICATIONS SERVICES FOR FORECASTING PURPOSES – STUDY OF THE IMPACT OF FACTORS NOT ARISING FROM THE CALENDAR

Keywords: Prediction System, telecommunications operator, subscriber group, category of connection

Abstract

The aim of this study is to identify the impact of factors (not arising from the calendar) on the demand for connection services offered by a telecommunications operator. The theoretical part of the research presents the importance of the Prediction System (PS) as a kind of Decision Support System in the operational management of the telecommunications operator. Theoretical aspects of PS structure have been included. Special attention has been paid to the statistical analysis module (as the PS subsystem), which was implemented in the adopted (researched) scope in the empirical part of the research. The empirical part presents the results of statistical analyses of demand for telecommunications services in the scope enabling identification of the impact of factors not arising from the calendar (i.e. the impact of category of connection and type of subscribers) on the level and distribution of such demand. The presented research results provide premises for the construction of forecasting tools, carrying out the forecasting procedure and monitoring the forecasts, i.e. they provide the necessary premises for the implementation of subsequent components of the PS.

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References

Black, K. (2010). Business Statistics for Contemporary Decision Making. John Wiley and Sons Inc., New York.

Daft, R.L., Marcic, D. (2011). Understanding Management. South-Western Cengage Learning, Mason.

Dittmann, P. (2004). Prognozowanie w przedsiębiorstwie. Metody i ich zastosowanie. Oficyna Ekonomiczna, Kraków.

Griffin, R. (2015). Fundamentals of Management. Cengage Learning, Boston.

Healey, J.F. (2012). Statistics: A Tool for Social Research. Wadsworth Cengage Learning, Belmont.

Kaczmarczyk, P. (2017). Microeconometric Analysis of Telecommunication Services Market with the Use of SARIMA Models. Dynamic Econometric Models, 17, 41-57. Doi: 10.12775/DEM.2017.003.

Kaczmarczyk, P. (2018). Neural Network Application to Support Regression Model in Forecasting Single-Sectional Demand for Telecommunications Services. Folia Oeconomica Stetinensia, 18, 159-177. Doi:10.1515/ foli-2016-0026.

Kasiewicz, S. (2005). Budowanie wartości firmy w zarządzaniu operacyjnym. Szkoła Główna Handlowa w Warszawie, Warszawa.

LeBlanc, D.C. (2004). Statistics: Concepts and Applications for Science. Jones and Bartlett Publisher, London.

Lee, Ch., Lee, J.C., Lee, A.C. (2013). Statistics for Business and Financial Economics. Springer, New York.

Luszniewicz, A., Słaby T. (2008). Statystyka z pakietem komputerowym Statistica PL. Teoria i zastosowanie. Wydawnictwo C.H. BECK, Warszawa.

Makridakis, S., Wheelwright, S.C., Hyndman, R.J. (1998). Forecasting Methods and Applications. J. Wiley, New York.

Marcinkowska, M. (2000). Kształtowanie wartości firmy. Wydawnictwo Naukowe PWN, Warszawa.

Muraszkiewicz, M. (2000). Eksploracja danych dla telekomunikacji. Retrieved from http://www.ploug.org.pl/showhtml.php?file=konf_00/materialy_00 [accessed: 02.07.2015].

Published
2019-03-30
How to Cite
Kaczmarczyk, P. (2019). STATISTICAL ANALYSIS OF DEMAND FOR TELECOMMUNICATIONS SERVICES FOR FORECASTING PURPOSES – STUDY OF THE IMPACT OF FACTORS NOT ARISING FROM THE CALENDAR. Acta Scientiarum Polonorum. Oeconomia, 18(1), 21-31. https://doi.org/10.22630/ASPE.2019.18.1.3