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

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Prediction of economic growth by extreme learning approach based on science and technology transfer
Petra Karanikic´1
• Igor Mladenovic´2
• Svetlana Sokolov-Mladenovic´2
•
Meysam Alizamir3
Springer Science+Business Media Dordrecht 2016
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.
Keywords GDP Forecasting Extreme learning machine Economic growth

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