The classification of Turkish economic growth by artificial neural network algorithms

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.