IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2402.01648.html
   My bibliography  Save this paper

Forecasting Imports in OECD Member Countries and Iran by Using Neural Network Algorithms of LSTM

Author

Listed:
  • Soheila Khajoui
  • Saeid Dehyadegari
  • Sayyed Abdolmajid Jalaee

Abstract

Artificial Neural Networks (ANN) which are a branch of artificial intelligence, have shown their high value in lots of applications and are used as a suitable forecasting method. Therefore, this study aims at forecasting imports in OECD member selected countries and Iran for 20 seasons from 2021 to 2025 by means of ANN. Data related to the imports of such countries collected over 50 years from 1970 to 2019 from valid resources including World Bank, WTO, IFM,the data turned into seasonal data to increase the number of collected data for better performance and high accuracy of the network by using Diz formula that there were totally 200 data related to imports. This study has used LSTM to analyse data in Pycharm. 75% of data considered as training data and 25% considered as test data and the results of the analysis were forecasted with 99% accuracy which revealed the validity and reliability of the output. Since the imports is consumption function and since the consumption is influenced during Covid-19 Pandemic, so it is time-consuming to correct and improve it to be influential on the imports, thus the imports in the years after Covid-19 Pandemic has had a fluctuating trend.

Suggested Citation

  • 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.
    • Handle: RePEc:arx:papers:2402.01648
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2402.01648
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heid, Benedikt & Vozzo, Isaac, 2020. "The international trade effects of bilateral investment treaties," Economics Letters, Elsevier, vol. 196(C).
    2. Haenlein, Michael & Kaplan, Andreas, 2021. "Artificial intelligence and robotics: Shaking up the business world and society at large," Journal of Business Research, Elsevier, vol. 124(C), pages 405-407.
    3. Fan, Dongyan & Sun, Hai & Yao, Jun & Zhang, Kai & Yan, Xia & Sun, Zhixue, 2021. "Well production forecasting based on ARIMA-LSTM model considering manual operations," Energy, Elsevier, vol. 220(C).
    4. 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.
    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. 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.
    7. Prior, Daniel D. & Keränen, Joona, 2020. "Revisiting contemporary issues in B2B marketing: It's not just about artificial intelligence," Australasian marketing journal, Elsevier, vol. 28(2), pages 83-89.
    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. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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).
    7. 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.
    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. Rahman, Muhammad Sabbir & Bag, Surajit & Gupta, Shivam & Sivarajah, Uthayasankar, 2023. "Technology readiness of B2B firms and AI-based customer relationship management capability for enhancing social sustainability performance," Journal of Business Research, Elsevier, vol. 156(C).
    18. 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.
    19. 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.
    20. 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.
    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:2402.01648. 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.