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Forecasting international bandwidth capacity using linear and ANN methods

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  • Madden, Gary G
  • Tan, Joachim

Abstract

An artificial neural network (ANN) can improve forecasts through pattern recognition of historical data. This article evaluates the reliability of ANN methods, as opposed to simple extrapolation techniques, to forecast Internet bandwidth index data that is bursty in nature. A simple feedforward ANN model is selected as a nonlinear alternative, as it is flexible enough to model complex linear or nonlinear relationships without any prior assumptions about the data generating process. These data are virtually white noise and provides a challenge to forecasters. Using standard forecast error statistics, the ANN and the simple exponential smoothing model provide modestly better forecasts than other extrapolation methods

Suggested Citation

  • Madden, Gary G & Tan, Joachim, 2008. "Forecasting international bandwidth capacity using linear and ANN methods," MPRA Paper 13005, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:13005
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    Cited by:

    1. João A. Bastos, 2019. "Forecasting the capacity of mobile networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(2), pages 231-242, October.
    2. Oscar Claveria & Enric Monte & Salvador Torra, 2014. "“A multivariate neural network approach to tourism demand forecasting”," IREA Working Papers 201417, University of Barcelona, Research Institute of Applied Economics, revised May 2014.
    3. 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

    Forecasting; international bandwidth capacity;

    JEL classification:

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

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