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Forecasting exports in selected OECD countries and Iran using MLP Artificial Neural Network

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  • Soheila Khajoui
  • Saeid Dehyadegari
  • Sayyed Abdolmajid Jalaee

Abstract

The present study aimed to forecast the exports of a select group of Organization for Economic Co-operation and Development (OECD) countries and Iran using the neural networks. The data concerning the exports of the above countries from 1970 to 2019 were collected. The collected data were implemented to forecast the exports of the investigated countries for 2021 to 2025. The analysis was performed using the Multi-Layer-Perceptron (MLP) neural network in Python. Out of the total number, 75 percent were used as training data, and 25 percent were used as the test data. The findings of the study were evaluated with 99% accuracy, which indicated the reliability of the output of the network. The Results show that Covid-19 has affected exports over time. However, long-term export contracts are less affected by tensions and crises, due to the effect of exports on economic growth, per capita income and it is better for economic policies of countries to use long-term export contracts.

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  • Soheila Khajoui & Saeid Dehyadegari & Sayyed Abdolmajid Jalaee, 2023. "Forecasting exports in selected OECD countries and Iran using MLP Artificial Neural Network," Papers 2312.15535, arXiv.org.
    • Handle: RePEc:arx:papers:2312.15535
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    References listed on IDEAS

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