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Unified knowledge based economy hybrid forecasting

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  • Al Shami, Ahmad
  • Lotfi, Ahmad
  • Coleman, Simeon
  • Dostál, Petr

Abstract

Many synthetic composite indicators have been developed with the aim to measure micro- and macro-knowledge competitiveness, however, without any unified, easy to visualise and assessable forecasting capability, their benefits to decision makers remain limited. In this article, a new framework for forecasting knowledge based economy (KBE) competitiveness is proposed. Existing KBE indicators from internationally recognised organisations are used to forecast and unify the KBE performance indices. Three different forecasting methods including time-series cross sectional (TSCS) (also known as panel data), linear multiple regression (LMREG), and artificial neural network (ANN) are employed. The ANN forecasting model outperformed the TSCS and LMREG. The proposed KBE hybrid forecasting model utilises a 2-stage ANN model which is fed with a panel data set structure. The first stage of the model consists of a feed-forward neural network that feeds to a Kohonen's self-organising map (SOM) in the second stage of the model. A feed-forward neural network is used to learn and predict the scores of nations using past observed data. Then, a SOM is used to aggregate the forecasted scores and to place nations in homogeneous clusters. The proposed framework can be applied in the context of forecasting and producing a unified meaningful map that places any KBE in its homogeneous league, even when considering a limited data set.

Suggested Citation

  • Al Shami, Ahmad & Lotfi, Ahmad & Coleman, Simeon & Dostál, Petr, 2015. "Unified knowledge based economy hybrid forecasting," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 107-123.
    • Handle: RePEc:eee:tefoso:v:91:y:2015:i:c:p:107-123
    DOI: 10.1016/j.techfore.2014.01.014
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    References listed on IDEAS

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    1. Lee, Kun Chang & Lee, Namho & Lee, Habin, 2012. "Multi-agent knowledge integration mechanism using particle swarm optimization," Technological Forecasting and Social Change, Elsevier, vol. 79(3), pages 469-484.
    2. Michael Freudenberg, 2003. "Composite Indicators of Country Performance: A Critical Assessment," OECD Science, Technology and Industry Working Papers 2003/16, OECD Publishing.
    3. Baker, Bruce D. & Richards, Craig E., 1999. "A comparison of conventional linear regression methods and neural networks for forecasting educational spending," Economics of Education Review, Elsevier, vol. 18(4), pages 405-415, October.
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    1. Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021. "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper 109137, University Library of Munich, Germany.

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