Soft computing prediction of economic growth based in science and technology factors

The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) to forecast the gross domestic product (GDP) growth rate. In this study the GDP growth was analyzed based on ten science and technology factors. These factors were: research and development (R&D) expenditure...

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Bibliographic Details
Published in:Physica A 2017-01, Vol.465, p.217-220
Main Authors: Marković, Dušan, Petković, Dalibor, Nikolić, Vlastimir, Milovančević, Miloš, Petković, Biljana
Format: Article
Language:English
Subjects:
GDP
Prediction
Science and technology factor
Soft computing
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Summary:The purpose of this research is to develop and apply the Extreme Learning Machine (ELM) to forecast the gross domestic product (GDP) growth rate. In this study the GDP growth was analyzed based on ten science and technology factors. These factors were: research and development (R&D) expenditure in GDP, scientific and technical journal articles, patent applications for nonresidents, patent applications for residents, trademark applications for nonresidents, trademark applications for residents, total trademark applications, researchers in R&D, technicians in R&D and high-technology exports. The ELM results were compared with genetic programming (GP), artificial neural network (ANN) and fuzzy logic results. Based upon simulation results, it is demonstrated that ELM has better forecasting capability for the GDP growth rate. •The estimation of the gross domestic product (GDP) growth rate.•Economic growth basis on combination of different factors.•The accuracy of the extreme learning machine (ELM).
ISSN:0378-4371
1873-2119
DOI:10.1016/j.physa.2016.08.034