IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2312.15535.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2312.15535
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alexander Keck & Alexander Raubold & Alessandro Truppia, 2010. "Forecasting international trade: A time series approach," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2009(2), pages 157-176.
    2. Morris Goldstein & Mohsin S. Khan, 2017. "The Supply and Demand for Exports: A Simultaneous Approach," World Scientific Book Chapters, in: TRADE CURRENCIES AND FINANCE, chapter 2, pages 83-104, World Scientific Publishing Co. Pte. Ltd..
    3. Garbellini, Nadia, 2021. "International trade as a process of choice of technique," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 42-50.
    4. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    5. Mekhail Mustak & Joni Salminen & Loïc Plé & Jochen Wirtz, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Post-Print hal-03269994, HAL.
    6. Gulshan Kumar & Sanjeev Gupta, 2010. "Forecasting Exports Of Industrial Goods From Punjab - An Application Of Univariate Arima Model," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 10(4), pages 169-180.
    7. Paschen, Jeannette & Wilson, Matthew & Ferreira, João J., 2020. "Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel," Business Horizons, Elsevier, vol. 63(3), pages 403-414.
    8. Mustak, Mekhail & Salminen, Joni & Plé, Loïc & Wirtz, Jochen, 2021. "Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 389-404.
    9. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    10. Sohrabpour, Vahid & Oghazi, Pejvak & Toorajipour, Reza & Nazarpour, Ali, 2021. "Export sales forecasting using artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Soheila Khajoui & Saeid Dehyadegari & Sayyed Abdolmajid Jalaee, 2024. "Forecasting Imports in OECD Member Countries and Iran by Using Neural Network Algorithms of LSTM," Papers 2402.01648, arXiv.org.
    2. Erik Hermann, 2022. "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective," Journal of Business Ethics, Springer, vol. 179(1), pages 43-61, August.
    3. Erik Karger & Marvin Jagals & Frederik Ahlemann, 2021. "Blockchain for Smart Mobility—Literature Review and Future Research Agenda," Sustainability, MDPI, vol. 13(23), pages 1-32, November.
    4. Reyes-Menendez, Ana & Clemente-Mediavilla, Jorge & Villagra, Nuria, 2023. "Understanding STI and SDG with artificial intelligence: A review and research agenda for entrepreneurial action," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    5. Hoffmann, Stefan & Lasarov, Wassili & Dwivedi, Yogesh K., 2024. "AI-empowered scale development: Testing the potential of ChatGPT," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    6. Raniah Alsahafi & Ahmed Alzahrani & Rashid Mehmood, 2023. "Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations," Sustainability, MDPI, vol. 15(5), pages 1-64, February.
    7. Mariani, Marcello M. & Hashemi, Novin & Wirtz, Jochen, 2023. "Artificial intelligence empowered conversational agents: A systematic literature review and research agenda," Journal of Business Research, Elsevier, vol. 161(C).
    8. Alexander Brem & Petra A. Nylund & Saeed Roshani, 2024. "Unpacking the complexities of crisis innovation: a comprehensive review of ecosystem-level responses to exogenous shocks," Review of Managerial Science, Springer, vol. 18(8), pages 2441-2464, August.
    9. Henrika Langen & Martin Huber, 2022. "How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign," Papers 2204.10820, arXiv.org, revised Jun 2022.
    10. Seyed Mohammad Ali Jafari & Ehsan Chitsaz, 2024. "Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market," Papers 2409.00658, arXiv.org.
    11. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    12. Mahmud, Hasan & Islam, A.K.M. Najmul & Mitra, Ranjan Kumar, 2023. "What drives managers towards algorithm aversion and how to overcome it? Mitigating the impact of innovation resistance through technology readiness," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    13. wael AL-khatib, Ayman, 2023. "Drivers of generative artificial intelligence to fostering exploitative and exploratory innovation: A TOE framework," Technology in Society, Elsevier, vol. 75(C).
    14. repec:bba:j00001:v:0:y:2024:i:1:p:1-:d:312 is not listed on IDEAS
    15. Debesh Mishra & Biswajit Mohapatra & Abhaya Sanatan Satpathy & Kamalakanta Muduli & Binayak Mishra & Swagatika Mishra & Upma Paliwal, 2024. "The pandemic COVID-19 and associated challenges with implementation of artificial intelligence (AI) in Indian agriculture," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(6), pages 2715-2729, June.
    16. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
    17. Shivam Gupta & Jakob Rhyner, 2022. "Mindful Application of Digitalization for Sustainable Development: The Digitainability Assessment Framework," Sustainability, MDPI, vol. 14(5), pages 1-23, March.
    18. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Vrontis, Demetris & Jabeen, Fauzia, 2022. "Digital transformation of organization using AI-CRM: From microfoundational perspective with leadership support," Journal of Business Research, Elsevier, vol. 153(C), pages 46-58.
    19. Blasco-Arcas, Lorena & Lee, Hsin-Hsuan Meg & Kastanakis, Minas N. & Alcañiz, Mariano & Reyes-Menendez, Ana, 2022. "The role of consumer data in marketing: A research agenda," Journal of Business Research, Elsevier, vol. 146(C), pages 436-452.
    20. Sudatta Kar & Arpan Kumar Kar & Manmohan Prasad Gupta, 2021. "Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(4), pages 217-238, October.
    21. Salminen, Joni & Kandpal, Chandrashekhar & Kamel, Ahmed Mohamed & Jung, Soon-gyo & Jansen, Bernard J., 2022. "Creating and detecting fake reviews of online products," Journal of Retailing and Consumer Services, Elsevier, vol. 64(C).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2312.15535. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.