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Forecasting wireless communication technologies


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  • Sabrina Patino
  • Jisun Kim
  • Tugrul U. Daim
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    The purpose of the paper is to present a formal comparison of a variety of multiple regression models in technology forecasting for wireless communication. We compare results obtained from multiple regression models to determine whether they provide a superior fitting and forecasting performance. Both techniques predict the year of wireless communication technology introduction from the first (1G) to fourth (4G) generations. This paper intends to identify the key parameters impacting the growth of wireless communications. The comparison of technology forecasting approaches benefits future researchers and practitioners when developing a prediction of future wireless communication technologies. The items of focus will be to understand the relationship between variable selection and model fit. Because the forecasting error was successfully reduced from previous approaches, the quadratic regression methodology is applied to the forecasting of future technology commercialisation. In this study, the data will show that the quadratic regression forecasting technique provides a better fit to the curve.

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    Bibliographic Info

    Article provided by Inderscience Enterprises Ltd in its journal Int. J. of Applied Management Science.

    Volume (Year): 2 (2010)
    Issue (Month): 2 ()
    Pages: 169-197

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    Handle: RePEc:ids:injams:v:2:y:2010:i:2:p:169-197

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    Keywords: technology forecasting; quadratic regression; wireless communications; variable selection; model fit; commercialisation; first generation; 1G; fourth generation; 4G; applied management science.;


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