Detail publikace

Unifed Knowledge Economy Hybrid Forecasting Map

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

Originální název

Unifed Knowledge Economy Hybrid Forecasting Map

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

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

Rok RIV

2014

Vydáno

22. 2. 2014

Nakladatel

GJTO

Místo

USA

ISSN

0040-1625

Periodikum

TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE

Ročník

91

Číslo

1

Stát

Spojené státy americké

Strany od

107

Strany do

123

Strany počet

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"
}