PREDICTING GLOBAL INTERNET GROWTH USING AUGMENTED DIFFUSION, FUZZY REGRESSION AND NEURAL NETWORK MODELS

Kallol Bagchi () and Somnath Mukhopadhyay ()
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Kallol Bagchi: Department of IDS, College of Business, The University of Texas at El Paso, El Paso, TX 79968-0544, USA
Somnath Mukhopadhyay: Department of IDS, College of Business, The University of Texas at El Paso, El Paso, TX 79968-0544, USA

International Journal of Information Technology & Decision Making (IJITDM), 2006, vol. 05, issue 01, 155-171

Abstract: Quantitative models explaining and forecasting the growth of new technology like the Internet in global business operation appear infrequently in the literature. This paper introduces two artificial intelligence (AI) models such as the neural network and fuzzy regression along with an augmented diffusion model to study and predict the Internet growth in several OECD nations. First, a linear version of an augmented diffusion model is designed. An augmented diffusion model is constructed by including an economic indicator, gross domestic product per capita, into the model. In the next step, two soft AI models are calibrated from the augmented diffusion model. Performance measures of predictions from these models on new samples show that these soft models provide improved forecast accuracy over the augmented diffusion model. The results confirm the major contribution of this research in predicting global Internet growth.

Keywords: Global Internet growth; forecasting; diffusion; fuzzy regression; neural network (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0219622006001861

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