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.
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07 February 2019
The Editor-in-Chief has retracted this article (Karaniki? et al. 2016) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap [most notably with the articles cited (Markovi? et al. 2016; Mladenovi? et al. 2016a, b)], peer review and authorship manipulation. All of the authors disagreed with the publication of this retraction.
07 February 2019
The Editor-in-Chief has retracted this article (Karaniki? et al. 2016) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap [most notably with the articles cited (Markovi? et al. 2016; Mladenovi? et al. 2016a, b)], peer review and authorship manipulation. All of the authors disagreed with the publication of this retraction.
07 February 2019
The Editor-in-Chief has retracted this article (Karaniki? et al. 2016) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap [most notably with the articles cited (Markovi? et al. 2016; Mladenovi? et al. 2016a, b)], peer review and authorship manipulation. All of the authors disagreed with the publication of this retraction.
07 February 2019
The Editor-in-Chief has retracted this article (Karaniki�� et al. 2016) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap [most notably with the articles cited (Markovi�� et al. 2016; Mladenovi�� et al. 2016a, b)], peer review and authorship manipulation. All of the authors disagreed with the publication of this retraction.
07 February 2019
The Editor-in-Chief has retracted this article (Karaniki�� et al. 2016) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap [most notably with the articles cited (Markovi�� et al. 2016; Mladenovi�� et al. 2016a, b)], peer review and authorship manipulation. All of the authors disagreed with the publication of this retraction.
07 February 2019
The Editor-in-Chief has retracted this article (Karaniki�� et al. 2016) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap [most notably with the articles cited (Markovi�� et al. 2016; Mladenovi�� et al. 2016a, b)], peer review and authorship manipulation. All of the authors disagreed with the publication of this retraction.
07 February 2019
The Editor-in-Chief has retracted this article (Karaniki�� et al. 2016) because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap [most notably with the articles cited (Markovi�� et al. 2016; Mladenovi�� et al. 2016a, b)], peer review and authorship manipulation. All of the authors disagreed with the publication of this retraction.
References
Abdullah, S.A., Malek, A.A., Abdullah, N.S., Kisi, O., Yap, K.S.: Extreme learning machines: a new approach for prediction of reference evapotranspiration. J. Hydrol. 527, 184–195 (2015)
Barsoum, F., Stankiewicz, S.: Forecasting GDP growth using mixed-frequency models with switching regimes. Int. J. Forecast. 31, 33–50 (2015)
Ćojbašić, Ž., Petković, D., Shamshirband, S., Tong, C.W., Ch, S., Janković, P., Dučić, N., Baralić, J.: Surface roughness prediction by extreme learning machine constructed with abrasive water jet. Precis. Eng. 43, 86–92 (2015). doi:10.1016/j.precisioneng.2015.06.013
Dias, F., Pinheiro, M., Rua, A.: Forecasting Portuguese GDP with factor models: pre- and post-crisis evidence. Econ. Model. 44, 266–272 (2015)
Feng, L., Zhang, J.: Application of artificial neural networks in tendency forecasting of economic growth. Econ Model 40, 76–80 (2014)
Ferrarini, B., Scaramozzino, P.: Production complexity, adaptability and economic growth. Struct. Change Econ. Dyn. 37, 52–61 (2016)
Huang, G.B., Zhu, Q.Y., Siew, C.K., 2003. Real-time learning capability of neural networks. Technical Report ICIS/45/2003, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: a new learning scheme of feedforward neural networks. Int. Joint Conf. Neural Netw. 2, 985–990 (2004)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme Learning Machine: theory and Applications. Neurocomputing 70, 489–501 (2006a)
Huang, G.B., Chen, L., Siew, C.K.: Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Netw. 17, 879–892 (2006b)
Jansen, W.J., Jin, X., de Winter, J.M.: Forecasting and nowcasting real GDP: comparing statistical models and subjective forecasts. Int. J. Forecast. 32, 411–436 (2016)
Kapetanios, G., Marcellino, M., Papailias, F.: Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods. Comput. Stat. Data Anal. (2015). doi:10.1016/j.csda.2015.02.017
Koza, J.: Genetic programming: on the programming of computers by natural selection. MIT Press, Cambridge (1992)
Krkoska, L., Teksoz, U.: Accuracy of GDP growth forecasts for transition countries: ten years of forecasting assessed. Int. J. Forecast. 23, 29–45 (2007)
Krkoska, L., Teksoz, U.: How reliable are forecasts of GDP growth and inflation for countries with limited coverage? Econ. Syst. 33, 376–388 (2009)
Liang, N.-Y., Huang, G.-B., Saratchandran, P., Sundararajan, N.: A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 17(6), 1411–1423 (2006)
Modis, T.: Long-term GDP forecasts and the prospects for growth. Technol. Forecast. Soc. Change. 80, 1557–1562 (2013)
Mohammadi, K., Shamshirband, S., Motamedi, S., Petkovic, D., Hashim, R., Gocic, M.: Extreme learning machine based prediction of daily dew point temperature. Comput. Electron. Agric. 117, 214–225 (2015a)
Mohammadi, K., Shamshirband, S., Yee, P.L., Petković, D., Zamani, M., Ch, S.: Predicting the wind power density based upon extreme learning machine. Energy. 86, 232–239 (2015b). doi:10.1016/j.energy.2015.03.111
Nian, R., He, B., Zheng, B., Heeswijk, M.V., Yu, Q., Miche, Y., et al.: Extreme learning machine towards dynamic model hypothesis in fish ethology research. Neurocomputing 128, 273–284 (2014)
Nikolić, V., Motamedi, S., Shamshirband, S., Petković, D., Ch, S., Arif, M.: Extreme learning machine approach for sensorless wind speed estimation. Mechatronics (2015). doi:10.1016/j.mechatronics.2015.04.007
Petković, D., Danesh, A.S., Dadkhah, M., Misaghian, N., Shamshirband, S., Zalnezhad, E., Pavlović, N.D.: Adaptive control algorithm of flexible robotic gripper by extreme learning machine. Robot. Computer-Integrated Manufact. 37, 170–178 (2016). doi:10.1016/j.rcim.2015.09.006
Shamshirband, S., Mohammadi, K., Tong, C.W., Petković, D., Porcu, E., Mostafaeipour, A., Ch, S., Sedaghat, A.: Application of extreme learning machine for estimation of wind speed distribution. Clim. Dyn. (2015a). doi:10.1007/s00382-015-2682-2
Shamshirband, S., Mohammadi, K., Yee, P.L., Petković, D., Mostafaeipour, A.: A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation. Renew. Sustain. Energy Rev 52, 1031–1042 (2015b). doi:10.1016/j.rser.2015.07.173
Wang, D., Huang, G.B.: Protein sequence classification using extreme learning machine. Proc. Int. Joint Conf. Neural Netw. 3, 1406–1411 (2005)
Zeira, J., Zoabi, H.: Economic growth and sector dynamics. Eur. Econ. Rev. 79, 1–15 (2015)
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The Editor-in-Chief has retracted this article [1] because validity of the content of this article cannot be verified. This article showed evidence of substantial text overlap (most notably with the articles cited [2, 3, 4]), peer review and authorship manipulation. All authors disagree. All of the authors disagreed with the publication of this retraction.
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1. “Prediction of economic growth by extreme learning approach based on science and technology transfer” Petra Karanikić, Igor Mladenović*, Svetlana Sokolov-Mladenović, Meysam Alizamir , Published 29 March 2016 https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1007_s11135-2D016-2D0337-2Dy&d=DwIFaQ&c=vh6FgFnduejNhPPD0fl_yRaSfZy8CWbWnIf4XJhSqx8&r=mImVkBpa54MY3KIaquiNBAiNRIkbDDT3A207pVhZWFk&m=6lQ4FOkv2cm-bbmO9NhTz4lpNXaI3XjlFq-JBGzzgVc&s=O1WO3uQCLvLZqs-NwgnKahkt8JZXTTlZK7uPAwFuDcQ&e=
2. “Estimation of the most influential science and technology factors for economic growth forecasting by soft computing technique” Dušan Marković, Igor Mladenović, Miloš Milovančević; Published 20 February 2016 https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1007_s11135-2D016-2D0321-2D6&d=DwIFaQ&c=vh6FgFnduejNhPPD0fl_yRaSfZy8CWbWnIf4XJhSqx8&r=mImVkBpa54MY3KIaquiNBAiNRIkbDDT3A207pVhZWFk&m=6lQ4FOkv2cm-bbmO9NhTz4lpNXaI3XjlFq-JBGzzgVc&s=qOrBEztXzKSspdg-HIxuw0tq0vBnjIuULrAxoe7ab5o&e=
3. “Analyzing of innovations influence on economic growth by fuzzy system”, Igor Mladenović, Miloš Milovančević, Svetlana Sokolov-Mladenović, Published 17 March 2016 https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1007_s11135-2D016-2D0331-2D4&d=DwIFaQ&c=vh6FgFnduejNhPPD0fl_yRaSfZy8CWbWnIf4XJhSqx8&r=mImVkBpa54MY3KIaquiNBAiNRIkbDDT3A207pVhZWFk&m=6lQ4FOkv2cm-bbmO9NhTz4lpNXaI3XjlFq-JBGzzgVc&s=Bv0GRr8RAYDNaSlqW6L3bI6ru7VeDm8onxyxy_mBYKE&e=
4. “Extreme learning approach with wavelet transform function for forecasting wind turbine wake effect to improve wind farm efficiency” IgorMladenović, DušanMarković, MilošMilovančević, MiroljubNikolić, Published 29 March 2016 https://urldefense.proofpoint.com/v2/url?u=https-3A__doi.org_10.1016_j.advengsoft.2016.02.011&d=DwIFaQ&c=vh6FgFnduejNhPPD0fl_yRaSfZy8CWbWnIf4XJhSqx8&r=mImVkBpa54MY3KIaquiNBAiNRIkbDDT3A207pVhZWFk&m=6lQ4FOkv2cm-bbmO9NhTz4lpNXaI3XjlFq-JBGzzgVc&s=VlNQBA4UruKf1W9a8NAr2mbnlzMt-e0Ri4qvrDAv8Qk&e=
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Karanikić, P., Mladenović, I., Sokolov-Mladenović, S. et al. RETRACTED ARTICLE: Prediction of economic growth by extreme learning approach based on science and technology transfer. Qual Quant 51, 1395–1401 (2017). https://doi.org/10.1007/s11135-016-0337-y
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DOI: https://doi.org/10.1007/s11135-016-0337-y