Forecasting of Export Volume Using Artificial Intelligence Based Algorithms

Abstract

Technological breakthroughs have transformed communication and taken transportation, health, and commerce to an unprecedented level. In this way, sudden developments have rapidly affected all countries. In this context, analysis methods are changing compared to the past, and annual analyses fail to catch the trend even for macroeconomic indicators. In this paper, new artificial intelligence-based estimation methods were used to see the future trend of export volume, and their estimation performances were compared by adding them to the classical econometric method. Historical quarterly data from 2013 to 2021 were used in the training and testing phases of the models. For this purpose, the variables of gross domestic product, foreign direct investment, and dollar exchange rate, which affect the export volume, were determined as inputs in estimating the export volume. According to the analysis results, support vector machine model was determined as the best method for predicting export volume in Turkey. This study can provide an essential basis for policymakers to export estimation and formulate their export-enhancing policies effectively.

Keywords

• Export Volume
• Forecasting
• SVM
• ANFIS
• MLR

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