Publication detail

Unifed Knowledge Economy Hybrid Forecasting Map

DOSTÁL, P. SHAMI, A. LOTFI, A. COLEMAN, S.

Original Title

Unifed Knowledge Economy Hybrid Forecasting Map

Type

journal article in Web of Science

Language

English

Original Abstract

Many synthetic composite indicators (SCI) has been developed to measure micro and macro knowledge competitiveness. Nonetheless, benefits to decision makers still limited due to numerous indicators, without any unifed, easy to visualize and evaluate forecasting capabilities. 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 diferent forecasting methods including Panel Data: time-series cross sectional (TSCS), Linear Multiple Regression (LMREG), and Artifcial Neural Network (ANN) are investigated. The ANN forecasting model outperformed the TSCS and LMREG. The proposed KBE forecasting model utilizes a 2-stage hybrid ANN model which are fed with panel data set structure. The first stage of the model consists of a feed-forward neural network that feeds to a Kohonen's Self-Organizing Map (SOM) in the second stage of the model. Feed-forward neural network is used to learn and predict the scores of nations using past observed data. Then, 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 unifed meaningful map that places any KBE in its homogeneous league considering limited dataset.

Keywords

Artifcial Neural Network; Self-Organizing Map; Panel Data Analysis; Knowledge Economy; Strategic Forecasting; Hybrid Forecasting Map; Principle Component Analysis.

Authors

DOSTÁL, P.; SHAMI, A.; LOTFI, A.; COLEMAN, S.

RIV year

2014

Released

22. 2. 2014

Publisher

GJTO

Location

USA

ISBN

0040-1625

Periodical

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE

Year of study

91

Number

1

State

United States of America

Pages from

107

Pages to

123

Pages count

34

URL

http://www.sciencedirect.com/science/article/pii/S0040162514000481

BibTex

@article{BUT105189,
  author="Petr {Dostál} and Ahmad Al {Shami} and Ahmad {Lotfi} and Simeon {Coleman}",
  title="Unifed Knowledge Economy Hybrid Forecasting Map",
  journal="TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE",
  year="2014",
  volume="91",
  number="1",
  pages="107--123",
  doi="10.1016/j.techfore.2014.01.014",
  issn="0040-1625",
  url="http://www.sciencedirect.com/science/article/pii/S0040162514000481"
}