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Forecasting telecommunications data with linear models

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  • Madden, Gary G
  • Tan, Joachim

Abstract

For telecommunication companies to successfully manage their business, companies rely on mapping future trends and usage patterns. However, the evolution of telecommunications technology and systems in the provision of services renders imperfections in telecommunications data and impinges on a company’s’ ability to properly evaluate and plan their business. ITU Recommendation E.507 provides a selection of econometric models for forecasting these trends. However, no specific guidance is given. This paper evaluates whether simple extrapolation techniques in Recommendation E.507 can generate accurate forecasts. Standard forecast error statistics—mean absolute percentage error, median absolute percentage error and percentage better—show the ARIMA, Holt and Holt-D models provide better forecasts than a random walk and other linear extrapolation methods.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 14739.

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Date of creation: 2007
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Publication status: Published in Telecommunications Policy 1.31(2007): pp. 31-44
Handle: RePEc:pra:mprapa:14739

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Keywords: linear models; ITU Recommendations; telecommunications forecasting;

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  1. Fildes, Robert & Hibon, Michele & Makridakis, Spyros & Meade, Nigel, 1998. "Generalising about univariate forecasting methods: further empirical evidence," International Journal of Forecasting, Elsevier, vol. 14(3), pages 339-358, September.
  2. Grambsch, Patricia & Stahel, Werner A., 1990. "Forecasting demand for special telephone services: A case study," International Journal of Forecasting, Elsevier, vol. 6(1), pages 53-64.
  3. Grubesic, Tony H. & Murray, Alan T., 2005. "Geographies of imperfection in telecommunication analysis," Telecommunications Policy, Elsevier, vol. 29(1), pages 69-94, February.
  4. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
  5. Everette S. Gardner, Jr. & Ed. Mckenzie, 1985. "Forecasting Trends in Time Series," Management Science, INFORMS, vol. 31(10), pages 1237-1246, October.
  6. Makridakis, Spyros & Chatfield, Chris & Hibon, Michele & Lawrence, Michael & Mills, Terence & Ord, Keith & Simmons, LeRoy F., 1993. "The M2-competition: A real-time judgmentally based forecasting study," International Journal of Forecasting, Elsevier, vol. 9(1), pages 5-22, April.
  7. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
  8. Fildes, Robert, 1992. "The evaluation of extrapolative forecasting methods," International Journal of Forecasting, Elsevier, vol. 8(1), pages 81-98, June.
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Cited by:
  1. Mack, Elizabeth A. & Grubesic, Tony H., 2009. "Forecasting broadband provision," Information Economics and Policy, Elsevier, vol. 21(4), pages 297-311, November.

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