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

Author

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

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.

Suggested Citation

  • Marković, Dušan & Petković, Dalibor & Nikolić, Vlastimir & Milovančević, Miloš & Petković, Biljana, 2017. "Soft computing prediction of economic growth based in science and technology factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 217-220.
    • Handle: RePEc:eee:phsmap:v:465:y:2017:i:c:p:217-220
    DOI: 10.1016/j.physa.2016.08.034
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    Cited by:

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    2. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    3. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    4. Sorić, Petar & Lolić, Ivana & Claveria, Oscar & Monte, Enric & Torra, Salvador, 2019. "Unemployment expectations: A socio-demographic analysis of the effect of news," Labour Economics, Elsevier, vol. 60(C), pages 64-74.
    5. Jia, Hongxiang & Li, Tianjiao & Wang, Anjian & Liu, Guwang & Guo, Xiaoqian, 2021. "Decoupling analysis of economic growth and mineral resources consumption in China from 1992 to 2017: A comparison between tonnage and exergy perspective," Resources Policy, Elsevier, vol. 74(C).
    6. Wu, Ya & Zhu, Qianwen & Zhu, Bangzhu, 2018. "Comparisons of decoupling trends of global economic growth and energy consumption between developed and developing countries," Energy Policy, Elsevier, vol. 116(C), pages 30-38.

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