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On the application of Machine Learning in telecommunications forecasting: A comparison
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On the application of Machine Learning in telecommunications forecasting: A comparison

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  • Petre, Konstantin
  • Varoutas, Dimitris

Abstract

Over the past few decades, a large number of research papers has published focused on forecasting ICT products using various diffusion models like logistic, Gompertz, Bass, etc. Much less research work has been done towards the application of time series forecasting in ICT such as ARIMA model which seems to be an attractive alternative. More recently with the advancement in computational power, machine learning and artificial intelligence have become popular due to superior performance than classical models in many areas of concern. In this paper, broadband penetration is analysed separately for all OECD countries, trying to figure out which model is superior in most cases and phases in time. Although diffusion models are dedicated for this purpose, the ARIMA model has nevertheless shown an enormous influence as a good alternative in many previous works. In this study, a new approach using LSTM networks stands out to be a promising method for projecting high technology innovations diffusion.

Suggested Citation

  • Petre, Konstantin & Varoutas, Dimitris, 2022. "On the application of Machine Learning in telecommunications forecasting: A comparison," 31st European Regional ITS Conference, Gothenburg 2022: Reining in Digital Platforms? Challenging monopolies, promoting competition and developing regulatory regimes 265665, International Telecommunications Society (ITS).
    • Handle: RePEc:zbw:itse22:265665
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    References listed on IDEAS

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    1. Bewley, Ronald & Fiebig, Denzil G., 1988. "A flexible logistic growth model with applications in telecommunications," International Journal of Forecasting, Elsevier, vol. 4(2), pages 177-192.
    2. Meade, Nigel & Islam, Towhidul, 1995. "Forecasting with growth curves: An empirical comparison," International Journal of Forecasting, Elsevier, vol. 11(2), pages 199-215, June.
    3. Gottardi, Giorgio & Scarso, Enrico, 1994. "Diffusion models in forecasting: A comparison with the Box-Jenkins approach," European Journal of Operational Research, Elsevier, vol. 75(3), pages 600-616, June.
    4. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
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    1. Petre, Konstantin & Chipouras, Aristides & Katsianis, Dimitris & Varoutas, Dimitris, 2023. "Anticipating High-Speed Broadband Penetration: A Multi-Country Pre-Launch Forecasting Study," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 278013, International Telecommunications Society (ITS).

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    More about this item

    Keywords

    Diffusion models; ARIMA; LSTM; broadband penetration forecasting;
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