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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.

Suggested Citation

  • Madden, Gary G & Tan, Joachim, 2007. "Forecasting telecommunications data with linear models," MPRA Paper 14739, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:14739
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    References listed on IDEAS

    as
    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.
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    6. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    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.
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    Citations

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    Cited by:

    1. Perambur Neelakanta & Raef Yassin, 2012. "Information theoretics-based technoeconomic forecasting: application to telecommunication service industry," Netnomics, Springer, vol. 13(1), pages 45-78, April.
    2. Paris A. Mastorocostas & Constantinos S. Hilas & Dimitris N. Varsamis & Stergiani C. Dova, 2016. "Telecommunications call volume forecasting with a block-diagonal recurrent fuzzy neural network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 63(1), pages 15-25, September.
    3. Shagun Srivastava & Madhvendra Misra, 2014. "Developing Evaluation Matrix for Critical Success Factors in Technology Forecasting," Global Business Review, International Management Institute, vol. 15(2), pages 363-380, June.
    4. Mack, Elizabeth A. & Grubesic, Tony H., 2009. "Forecasting broadband provision," Information Economics and Policy, Elsevier, vol. 21(4), pages 297-311, November.
    5. Meade, Nigel & Islam, Towhidul, 2015. "Forecasting in telecommunications and ICT—A review," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1105-1126.

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    More about this item

    Keywords

    linear models; ITU Recommendations; telecommunications forecasting;
    All these keywords.

    JEL classification:

    • L96 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Telecommunications

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