Comparative analysis of types of neural networks for solving problems of modeling socioeconomic systems (forecasting of production using neural networks, for example, on an LSTM-type network)
References
- Arman Ahmadi, Andre Daccache, Mojtaba Sadegh, Richard L. Snyder. Statistical and deep learning models for reference evapotranspiration time series forecasting: A comparison of accuracy, complexity, and data efficiency. Computers and Electronics in Agriculture, Volume 215, 2023, 108424, ISSN 0168-1699, DOI: 10.1016/j.compag.2023.108424.
- Baeva N. B., Kurkin E. V. Ed. Fundamentals of systems theory and computational schemes of system analysis: methodical manual (2018). Voronezh State Univ. compiled by: revised and supplemented. Electronic text data. Voronezh: VSU Publishing House.
- Baeva N. B., Kurkin, E. V. (2018) Stability of the economic system of the region to the impact of production lacuna. Proceedings of VSU, series: System analysis and information technologies. No. 4. P. 81–89. DOI: 10.17308/sait.2019.4/2686 (in Russian).
- Bengio Y. (2012) Deep learning of representations for unsupervised and transfer learning. Y. Bengio. JMLR W&CP: Proc. Unsupervised and Transfer Learning.
- Bengio Y. (2013) Representation Learning: A Review and New Perspectives. Y. Bengio, A. Courville, P. Vincent. IEEE Transactions on Pattern Analysis and Machine Intelligence. V. 35. – P. 1798–1828. – DOI: 10.1109/TPAMI.2013.50.3.
- Carpenter G.A., Grossberg S. (1987) ART 2: Self-organization of stable category recognition codes for analog input patterns. Applied optics. 26(23): 4919–4930.
- Donskikh A. O., Sirota, A. A. (2019) Training of deep neural networks under small sample conditions for classification of biological objects by multispectral measurements. Vestnik VSU, Series: System Analysis and Information Technologies. no. 4. p. 109–118 (in Russian).
- Guangxu Chen, Hailong Tian, Ting Xiao, Tianfu Xu, Hongwu Lei. Time series forecasting of oil production in Enhanced Oil Recovery system based on a novel CNN-GRU neural network. Geoenergy Science and Engineering, Volume 233, 2024, 212528, ISSN 2949–8910, DOI: 10.1016/j.geoen.2023.212528.
- Guo Q., He, Z. & Wang, Z. Monthly climate prediction using deep convolutional neural network and long short-term memory. Sci Rep 14, 17748 (2024). DOI: 10.1038/s41598-024-68906-6
- Haque M. S. (2023). Retail Demand Forecasting Using Neural Networks and Macroeconomic Variables. Journal of Mathematics and Statistics Studies, 4(3), 01–06. DOI: 10.32996/jmss.2023.4.3.1
- Hebb D. O. (1949) The organization of behavior; a neuropsychological theory. Publisher. New York, Wiley. 319 p.
- Hecht-Nielsen, R. (1987) Kolmogorov’s mapping neural network existence theorem. Int. Conf NN, IEEE Press, v. III, pp.11–13.
- Hopfield J.J. (1982) Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings of the National Academy of Sciences. Vol. 79(8): 2554–2558. DOI:10.1073/pnas.79.8.2554
Https://rosstat.gov.ru/rosstat.gov.ru – official website of the Federal State Statistics Service.- Ian Goodfellow, Yoshua Bengio, Aaron Courville (2016) Deep Learning (Adaptive Computation and Machine Learning series). Publisher: The MIT Press (November 18). 800 p.
- João N.C. Gonçalves, Paulo Cortez, M. Sameiro Carvalho, Nuno M. Frazão. A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain. Decision Support Systems, Volume 142, 2021, 113452, ISSN 0167-9236, DOI: 10.1016/j.dss.2020.113452.
- Johnson, Jaya. (2023) Machine Learning for Financial Market Forecasting. Master’s thesis. Harvard University Division of Continuing Education.
- Jonathan Richetti, Foivos I. Diakogianis, Asher Bender, André F. Colaço, Roger A. Lawes. A methods guideline for deep learning for tabular data in agriculture with a case study to forecast cereal yield. Computers and Electronics in Agriculture, Volume 205, 2023, 107642, ISSN 0168-1699, DOI: 10.1016/j.compag.2023.107642.
- Kohonen T. (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics. № 43. P. 59–69.
- Krizhevsky A. (2012) ImageNet classification with deep convolutional neural networks. A. Krizhevsky, I. Sutskever, G. Hinton. In Proc. Advances in Neural Information Processing Systems. V. 25. P. 1090–1098. DOI: 10.1145/3065386.
- Larichev O. I. (1996) Qualitative methods of decision making. M. Nauka, Fizmalit (in Russian).
- LeCun Y. Deep (2015) Learning. Y. LeCun, Y. Bengio, G. Hinton. Nature. V. 521. P. 436–444. DOI: 10.1038/nature14539.
- Martin T Hagan, Howard B Demuth, Mark H Beale, Orlando De Jesús. (2014) Neural Network Design (2nd Edition). Publisher. Martin Hagan. 2nd ed. edition (September 1). 800 p. (in English).
- Nath Fatick & Chowdhury, Mohammed Omar Sahed & Rhaman, Md. (2023). Navigating Produced Water Sustainability in the Oil and Gas Sector: A Critical Review of Reuse Challenges, Treatment Technologies, and Prospects Ahead. Water. 15. 4088. 10.3390/w15234088.
- Neumann J. (1958) The computing machine and the brain. New Haven: Yale University Press. 96.
- Nguyen Tuan & Haider, Mahfuz & Jisan, Afjal & Raju, Md Azad Hossain & Imam, Touhid & Khan, Md & Al-Jarf, Abdullah. (2024). Product Demand Forecasting with Neural Networks and Macroeconomic Indicators: A Comparative Study among Product Categories. Journal of Business and Management Studies. 6. 170–175. 10.32996/jbms.2024.6.2.17.
- Ozlem Karahasan, Eren Bas, Erol Egrioglu, A hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism, Information Sciences, Volume 686, 2025, 121356, ISSN 0020-0255, DOI: 10.1016/j.ins.2024.121356. (
https://www.sciencedirect.com/science/article/pii/S0020025524012702 at 2024-10-12) - Quiñones, Hector & Rubiano, Oscar & Alfonso, Wilfredo. (2023). Demand forecasting using a hybrid model based on artificial neural networks: A study case on electrical products. Journal of Industrial Engineering and Management. 16. 363. 10.3926/jiem.3928.
- Raul Rojas. (1996) Neural Networks: A Systematic Introduction. Publisher. Springer; 1st edition (July 12). 522 p. (in English).
- Sebastian Raschka Python Machine Learning (2015) 1st Edition. Publisher: Packt Publishing (September 1). 454 p.
- Sedykh I. A., Istomin, V. A. (2019) Neural network modeling of the strip cooling process at the hot rolling mill. Proceedings of VSU, series: System analysis and information technologies. No. 2. P. 116–125. DOI: 10.17308/sait.2019.4/2686
- Stéphane Goutte, Klemens Klotzner, Hoang-Viet Le, Hans-Jörg von Mettenheim, Forecasting photovoltaic production with neural networks and weather features, Energy Economics, Volume 139, 2024, 107884, ISSN 0140-9883, DOI: 10.1016/j.eneco.2024.107884.
- Stuart Russell, Peter Norvig (2013) Artificial Intelligence: Pearson New International Edition: A Modern Approach. Publisher. Pearson. 3rd edition (5 Aug.). 1104 p.
- Tariq Rashid. (2016) Make Your Own Neural Network. Publisher. CreateSpace Independent Publishing Platform (March 31). 222 pages.
- Toby Segaran. (2011) Programming. Collective Intelligence: Building Smart Web 2.0 Applications [Paperback]. Segaran, Toby Paperback – 1 January 2011. Publisher: Shroff/O’Reilly; First Edition (1 January). 384 pages.
- Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien, Lin (2012). Learning From Data Hardcover. Publisher. AMLBook (January 1,). 213 pages.
DOI: https://doi.org/10.2478/ijssis-2026-2018 | Journal eISSN: 1178-5608
Language: English
Submitted on: Apr 4, 2024
Published on: Mar 15, 2026
Published by: Macquarie University, Australia
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
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© 2026 Tatiana A. Makarenya, Ali Sajae Mannaa, Alexey I. Kalinichenko, Svetlana V. Petrenko, published by Macquarie University, Australia
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