Advertisement

The Classification of Turkish Economic Growth by Artificial Neural Network Algorithms

  • Yeliz KaracaEmail author
  • Şengül Bayrak
  • Emrullah Fatih Yetkin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10405)

Abstract

The development of globalization means that economies of the world are placing more importance on international trade. The increase in the variety of goods or services and the implementation of deregulation policies by many countries have made increases in international trade unavoidable. This study investigates the relationship between Turkey’s international trade balance and economic growth between the years of 1960 and 2015 by using data obtained by the Turkish Statistical Institute. Two data sets were used in this study to identify the factors that affect the Turkish international trade balance and economic growth. Data Set 1 was formed by combining the parameters that make up international trade balance and those that determine economic growth. Data Set 2 consists only of parameters that define international trade balance. The aim of this study is to be able to identify the relationship between the parameters that define international trade balance and economic growth in a computerized system. In order to achieve this, Data Set 1 and Data Set 2 were subjected to various Artificial Neural Network methods such as Feed Forward Back Propagation and Cascade Forward Back Propagation algorithms and were classified in accordance with the international trade volume parameters. At the conclusion of the experimental work, the accuracy of the Feed Forward Back Propagation and Cascade Forward Back Propagation algorithms obtained from the test operation in the classification process was calculated. As a result, the study has classified the factors that influence the growth of the economy and international trade in Turkey.

Keywords

Artificial neural networks Feed Forward Back Propagation algorithm Cascade Forward Back Propagation algorithm Economic growth 

References

  1. 1.
    Jorgenson, D., Gollop, F.M., Fraumeni, B.: Productivity and US Economic Growth, p. 169. Elsevier, Amsterdam (2016)Google Scholar
  2. 2.
    Solow, R.M.: Resources and economic growth. Am. Econ. 61(1), 52–60 (2016)Google Scholar
  3. 3.
    Lu, X., Guo, K., Dong, Z., Wang, X.: Financial development and relationship evolvement among money supply, economic growth and inflation: a comparative study from the US and China. Appl. Econ. 49(10), 1032–1045 (2017)CrossRefGoogle Scholar
  4. 4.
    Greiner, A., Semmler, W., Gong, G.: The Forces of Economic Growth: A Time Series Perspective. Princeton University Press, Princeton (2016)Google Scholar
  5. 5.
    Srinivasan, A., Jayalakshmi, G.: Probabilistic analysis on time to recruitment for a single grade man power system when the breakdown threshold has two components using a different policy of recruitment. Indian J. Appl. Res. 5(7), 1–3 (2016)Google Scholar
  6. 6.
    Jacop, M.W.: The intensity of trade creation and trade diversion in COMESA, ECCAS and ECOWAS: a comparative analysis. J. Afr. Econ. 14(1), 117–141 (2005)CrossRefGoogle Scholar
  7. 7.
    Aguilar, C.A.: Trade analysis of specific agri-food commodities using a gravity model, michigan state university department of agricultural economics. Master of Science Thesis, Michigan (2006)Google Scholar
  8. 8.
    Dimitri, D.G., Balazs, H., Elina, R.: Foreign direct investment in European transition economies - the role of policies. IMF Working Papers 20431, 26–42 (2007)Google Scholar
  9. 9.
    Tenreyro, S.: On the trade impact of nominal exchange rate volatility. J. Dev. Econ. 82(2), 4 (2007)CrossRefGoogle Scholar
  10. 10.
    Allen, J.T.: The foreign direct investment-exports relationship: a US-Mexico analysis using the gravity model. Northern Illinois University, Doctor of Philosophy Dissertation, Dekalb, Illinois (2007)Google Scholar
  11. 11.
    Arize, A.C., Osang, T., Slottje, D.J.: Exchange-rate volatility in Latin America and its impact on foreign trade. Int. Rev. Econ. Finan. 17, 33–44 (2008)CrossRefGoogle Scholar
  12. 12.
    Kayumova, N.O.: How Exchange Rate Volatility Affects on the Main Export Goods of Uzbekistan? (2013). doi: 10.12955/ejbe.v5i0.166
  13. 13.
    Wang, S.C.: Artificial neural network. In: Wang, S.C. (ed.) Interdisciplinary Computing in Java Programming, vol. 743, pp. 81–100. Springer, US (2003)CrossRefGoogle Scholar
  14. 14.
    Akerkar, R., Sajja, P.: Knowledge-Based Systems. Jones & Bartlett Publishers (2010)Google Scholar
  15. 15.
    Mila\(\check{c}\)i\(\grave{c}\), L., Jovi\(\grave{c}\), S., Vujovi\(\grave{c}\), T., Miljkovi\(\grave{c}\), J.: Application of artificial neural network with extreme learning machine for economic growth estimation. Phys. A Stat. Mech. Appl. 465, 285–288 (2017)Google Scholar
  16. 16.
    Skiba, M., Mrówczyńska, M., Bazan-Krzywoszańska, M.: Modeling the economic dependence between town development policy and increasing energy effectiveness with neural networks. Case study: the town of Zielona Gra. Appl. Energy 188, 356–366 (2017)CrossRefGoogle Scholar
  17. 17.
    Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques, pp. 398–406. Elsevier, Amsterdam (2011)Google Scholar
  18. 18.
    Wang, L., Zeng, Y., Chen, T.: Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst. Appl. 42(2), 855–863 (2015)CrossRefGoogle Scholar
  19. 19.
    Karaca, Y., Hayta, Ş.: Application and comparison of ANN and SVM for diagnostic classification for cognitive functioning. Appl. Math. Sci. 10(64), 3187–3199 (2016)Google Scholar
  20. 20.
    Qiao, J., Li, F., Han, H., Li, W.: Constructive algorithm for fully connected cascade feedforward neural networks. Neurocomputing 182, 154–164 (2016)CrossRefGoogle Scholar
  21. 21.
    Qiu, M., Song, Y., Akagi, F.: Application of artificial neural network for the prediction of stock market returns: the case of the Japanese stock market. Chaos Solitons Fractals 85, 1–7 (2016)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Peters, E.E.: Fractal Market Analysis Applying Chaos Theory to Investment and Economics. Wiley, Hoboken (1994)Google Scholar
  23. 23.
    Li, M.: Fractal time series—a tutorial review. Math. Probl. Eng. 2010 (2009). doi: 10.1155/2010/157264
  24. 24.
    Loffredo, M.I.: Testing chaos and fractal properties in economic time series. In: International Mathematica Symposium (1999)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yeliz Karaca
    • 1
    Email author
  • Şengül Bayrak
    • 2
  • Emrullah Fatih Yetkin
    • 3
  1. 1.Visiting Engineering School (DEIM)Tuscia UniversityViterboItaly
  2. 2.Department of Computer EngineeringHalic UniversityIstanbulTurkey
  3. 3.Department of Computer EngineeringKemerburgaz UniversityIstanbulTurkey

Personalised recommendations