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

Dušan Marković, Dalibor Petković, Vlastimir Nikolić, Miloš Milovančević and Biljana Petković

Physica A: Statistical Mechanics and its Applications, 2017, vol. 465, issue C, 217-220

Abstract: 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.

Keywords: Soft computing; GDP; Prediction; Science and technology factor (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:465:y:2017:i:c:p:217-220

DOI: 10.1016/j.physa.2016.08.034

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