Prediction of economic growth by extreme learning approach based on science and technology transfer

Prediction of economic growth by extreme learning approach based on science and technology transfer The purpose of this research is to develop and apply the extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. Economic growth may be developed on the basis on combination of different factors. In this investigation was analyzed the economic growth prediction based on the science and technology transfer. The main goal was to analyze the influence of number of granted European patents on the economic growth by field of technology. GDP was used as economic growth indicator. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and using several statistical indicators. Coefficient of determination for ELM method is 0.9841, for ANN method it is 0.7956 and for the GP method it is 0.7561. Based upon simulation results, it is demonstrated that ELM can be utilized effectively in applications of GDP forecasting. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Prediction of economic growth by extreme learning approach based on science and technology transfer

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Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer Science+Business Media Dordrecht
Subject
Social Sciences; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-016-0337-y
Publisher site
See Article on Publisher Site

Abstract

The purpose of this research is to develop and apply the extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. Economic growth may be developed on the basis on combination of different factors. In this investigation was analyzed the economic growth prediction based on the science and technology transfer. The main goal was to analyze the influence of number of granted European patents on the economic growth by field of technology. GDP was used as economic growth indicator. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and using several statistical indicators. Coefficient of determination for ELM method is 0.9841, for ANN method it is 0.7956 and for the GP method it is 0.7561. Based upon simulation results, it is demonstrated that ELM can be utilized effectively in applications of GDP forecasting.

Journal

Quality & QuantitySpringer Journals

Published: Mar 29, 2016

References

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