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References

  1. Dynkin, A.A. Science of foresight: how to succeed in strategic forecasting and planning/ A.A. Dynkin, V.D. Milovidov// Problems of Forecasting. - 2023. - No 3(198). - C. 6–23. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.47711/0868-6351-198-6-23." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.47711/0868-6351-198-6-23.</a></pub-id>
  2. Lazhentsev, V.N. Program-targeted resource mobilization/ V.N. Lazhentsev// Problems of forecasting. - 2023. - No 1(196). - C. 32–41. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.47711/0868-6351-196-32-41" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.47711/0868-6351-196-32-41</a></pub-id>.
  3. Modeling of development of the industrial complex of the Southern Federal District/ Makarenya, T.A. [et al.] - Ufa: Scientific and Publishing Center “Aeterna”, 2023. - 127 c.
  4. Blokhin, A.A. Global challenges for the system of strategic planning in Russia/ A.A. Blokhin, D.B. Kuvalin. Problems of forecasting. - 2023. - No 3(198). C. 24–41. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.47711/0868-6351-198-24-41" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.47711/0868-6351-198-24-41</a></pub-id>. (In Russia).
  5. Kasparyants, D. Analysis of the artificial intelligence market in 2021. Scientific and Technical Center of FSUE “Main Radio Frequency Center”. 30.11.2021. URL: <a href="https://clck.ru/35cyFZ/" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://clck.ru/35cyFZ/</a>.
  6. Official website of the federal service of state statistics. Mode of access: <a href="https://rosstat.gov.ru/" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://rosstat.gov.ru/</a>
  7. Everette, S., Exponential smoothing: The state of the art/ S. Everette, Jr. Gardner// Journal of forecasting. – 1985 -T. 4. - No 1. - P. 1–28.
  8. Alvin C. Rencher. Methods of Multivariate Analysis. Wiley Series in Probability and Statistics/ Alvin C. Rencher, Christensen, William F.// Multivariate regression. Section 10.1. Introduction. - 2012 - Chapter 10, vol. 709.
  9. Hamilton, J. Time Series Analysis/ J. Hamilton; Princeton University Press. -1994 - ISBN 9780691042893.
  10. Chaudhary, K., Machine learning-based mathematical modelling for prediction of social media consumer behavior using big data analytics/ K. Chaudhary, M. Alam, A.S. Mabrook, A. Gumaei// Journal of Big Data. 8 (1) – 73 – 2021 doi:<pub-id pub-id-type="doi"><a href="https://doi.org/10.1186/s40537-021-00466-2" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1186/s40537-021-00466-2</a></pub-id>. ISSN 2196-1115.
  11. Winters, P. R. Forecasting Sales by Exponentially Weighted Moving Averages// Management Science. – 1960 - 6 (3). - 324–342. doi:<pub-id pub-id-type="doi"><a href="https://doi.org/10.1287/mnsc.6.3.324." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1287/mnsc.6.3.324.</a></pub-id>
  12. Hastie, T. The elements of statistical learning: data mining, inference, and prediction/ T. Hastie; New York: Springer. 2001. ISBN 0-387-95284-5. OCLC 46809224.
  13. Guerci, J.R. Space-Time Adaptive Processing for Radar/ J.R. Guerci - Artech House Publishers. - 2003. ISBN 1-58053-377-
  14. Zhang, G.P. Neural Networks for Time-Series Forecasting/ Zhang, G.P. [and etc.]// In: Handbook of Natural Computing. Springer, Berlin, Heidelberg. – 2012. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.1007/978-3-540-92910-9_14" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/978-3-540-92910-9_14</a></pub-id>
  15. Etuk, Ette. An Additive SARIMA Model for Daily Exchange Rates of the Malaysian Ringgit (MYR) and Nigerian Naira (NGN)/ Ette Etuk. International Journal of Empirical Finance. – 2014 - 2(4). - vol. 2. - pages 193–201.
  16. Time series forecasting with multiple candidate models: selecting or combining/[and etc.]// Yu, L. Journal of Systems Science and Complexity. – 2005. -18(1). - pp.1–18.
  17. Donnelly, J., Forecasting global climate drivers using Gaussian processes and convolutional auto-encoders/ J. Donnelly, A. Daneshkhah, S. Abolfathi// Engineering Applications of Artificial Intelligence. - Vol. 128. – 2024 - ISSN 0952-1976, DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.1016/j.engappai.2023.10753" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.engappai.2023.10753</a></pub-id>
  18. Geweke, J. Chapter 1 Bayesian Forecasting/ J. Geweke, C. Whiteman// Handbook of Economic Forecasting. - Elsevier. – 2006 - Vol. 1. Pages 3–80. ISSN 1574-0706, ISBN 9780444513953. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.1016/S1574-0706(05)01001-3" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/S1574-0706(05)01001-3</a></pub-id>.
  19. Aksoy, N. Predictive models development using gradient boosting based methods for solar power plants/ N. Aksoy, I. Genc// Journal of Computational Science. Vol. 67. – 2023 - ISSN 1877-7503, DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.1016/j.jocs.2023.101958." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.jocs.2023.101958.</a></pub-id>
  20. Grossman, I./ I. Grossman, Wilson T., J. Temple// Forecasting small area populations with long short-term memory networks. - Socio-Economic Planning Sciences. - 2023. - Vol. 88. ISSN 0038-0121, DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.1016/j.seps.2023.101658" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.seps.2023.101658</a></pub-id>
  21. Bach, F. R. Learning Graphical Models for Stationary Time Series/ F. R. Bach, M. I. Jordan// Ieee transactions on signal processing’s. - 2004. - VOL. 52. - NO. 8. - pages 2189–2199.
  22. Nowotarski, J. Computing electricity spot price prediction intervals using quantile regression and forecast averaging/ J. Nowotarski, R. Weron// Computational Statistics. – 2015. - 30 (3). 791–803. doi:<pub-id pub-id-type="doi"><a href="https://doi.org/10.1007/s00180-014-0523-0" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1007/s00180-014-0523-0</a></pub-id>. ISSN 0943-4062.
  23. Seawright, J. The Case for Selecting Cases That Are Deviant or Extreme on the Independent Variable/J. Seawright, Sociological Methods &amp; Research, 2016 45(3). - 493–525. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.1177/0049124116643556" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1177/0049124116643556</a></pub-id>
  24. Decomposition forecasting methods: A review of applications in power systems/ N. Mbuli [and etc.]// Energy Reports. – 2020 - Vol. 6. Supp. 9. Pages 298–306, ISSN 2352-4847. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.1016/j.egyr.2020.11.238" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1016/j.egyr.2020.11.238</a></pub-id>
  25. Jin, M., Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection/ Jin M., Koh H.Y., Wen Q. A.// JOURNAL OF LATEX CLASS FILES. – 2021. - VOL. 14. - NO. 8. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.48550/arXiv.2307.03759." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.48550/arXiv.2307.03759.</a></pub-id>
  26. Nguyen, N. Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting/ N. Nguyen, Quanz B.// Proceedings of the AAAI. Conference on Artificial Intelligence. 35. -2021. - 9117–9125. <a href="https://doi.org/10.1609/aaai.v35i10.17101." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1609/aaai.v35i10.17101.</a>
  27. Gorodnova, N.V. Modeling the development and implementation of systems of “weak” and “strong” artificial intelligence: socio-economic aspects/ N.V. Gorodnova// Voprosy innovatsionnymi ekonomiki.-2022. - T. 12. - No 1. - C. 123–140. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.18334/vinec.12.1.113717." target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.18334/vinec.12.1.113717.</a></pub-id>
  28. Fahle, S. Systematic review on machine learning (ML) methods for manufacturing processes–Identifying artificial intelligence (AI) methods for field application/S. Fahle, C. Prinz, B. Kuhlenkötter// Procedia CIRP. – 2020 - 93. pp. 413–418.
  29. Apurvanand, S. Integration of Prophet Model and Convolution Neural Network on Wikipedia Trend Data/S. Apurvanand, J. Amudha// Journal of Computational and Theoretical Nanoscience. 17. – 2020. - pages 260–266. <pub-id pub-id-type="doi"><a href="https://doi.org/10.1166/jctn.2020.8660" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.1166/jctn.2020.8660</a></pub-id>.
  30. Effective domestic practices based on artificial intelligence technologies in the manufacturing industry. Analytical report. ANO “Digital Economy”. 2022. URL: <a href="https://clck.ru/35cx4s" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://clck.ru/35cx4s</a>.
  31. News site. - Access mode: <a href="https://nauka.tass.ru/nauka/19908027" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://nauka.tass.ru/nauka/19908027</a>. (In Russia).
  32. Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data/ W.J. Lee [and etc.]// Procedia CIRP. – 2019. pp. 506–511.
  33. A survey on artificial intelligence in Chinese sign language recognition/ X. Jiang [and etc.]// Arabian J. Sci. – 2020 - Eng. 45. pp. 9859–9894.
  34. Artificial Intelligence Methodologies for Data Management/ Serey, J. [and etc.]// Symmetry. – 2021. - 13. - 2040. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.3390/sym13112040" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3390/sym13112040</a></pub-id>
  35. He, S. Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases/ S. He, L.G. Leanse, Y. Feng// Adv. Drug Deliv. – 2021 - Rev. 178. Article 113922.
  36. Wasilow, S. Artificial intelligence, robotics, ethics, and the military: a Canadian perspective/ S. Wasilow, J.B. Thorpe// AI Magazine. – 2019. – 40.pp. 37–48.
  37. Data-driven artificial intelligence applications for sustainable precision agriculture/ M.T. Linaza [and etc.]// Agronomy. – 2021 - 11. - p. 1227.
  38. Belk, R. Ethical issues in service robotics and artificial intelligence./ R. Belk// Serv. Ind. - 2021.- 41. -pp. 860–876.
  39. Sako, K. Neural Networks for Financial Time Mpinda/ K. Sako, PC. Rodrigues// Series Forecasting. Entropy (Basel). – 2022 - May 7. - 24(5). - 657. doi: <pub-id pub-id-type="doi"><a href="https://doi.org/10.3390/e24050657" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.3390/e24050657</a></pub-id>. PMID: <pub-id pub-id-type="pmid">35626542</pub-id>. PMCID: <pub-id pub-id-type="pmcid">PMC9141105</pub-id>.
  40. Ekhlakov, R.S. Forecasting the cost of quotes using LSTM and GRU networks/ R.S. Ekhlakov, V.A. Sudakov// Preprints of M.V. Keldysh IPM. - 2022. - No 17. 13 c. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.20948/prepr-2022-17" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.20948/prepr-2022-17</a></pub-id> <a href="https://library.keldysh.ru/preprint.asp?id=2022-17" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://library.keldysh.ru/preprint.asp?id=2022-17</a>.
  41. Artificial intelligence and the future of surgical robotics/ Panesar, S. [and etc.]// Ann. Surg. – 2019. -270. pp. 223–226.
  42. Ganascia, J.-G. Artificial intelligence: between myth and reality/ J.-G. Ganascia// The UNESCO Courier. – 2018. - No. 3. URL: <a href="https://clck.ru/35cxVw" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">https://clck.ru/35cxVw</a>.
  43. Vorontsova, I.V. The definition of “artificial intelligence” and its semantic-procedural meaning in the judicial system of Russia and foreign countries/ I.V. Vorontsova, Y.A. Lukonina// Russian judge. - 2020. - No 10. - C. 41–45. Doi: <pub-id pub-id-type="doi"><a href="https://doi.org/10.18572/1812-3791-2020-10-41-45" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.18572/1812-3791-2020-10-41-45</a></pub-id>.
  44. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastin/ Xingjian Shi [and etc.]// (or arXiv:1506.04214v2 [cs.CV] for this version). -2015. – 13. DOI: <pub-id pub-id-type="doi"><a href="https://doi.org/10.48550/arXiv.1506.04214" target="_blank" rel="noopener noreferrer" class="text-signal-blue hover:underline">10.48550/arXiv.1506.04214</a></pub-id>.
  45. Ho, T.K. The Random Subspace Method for Constructing Decision Forests (PDF)/ T.K. Ho// IEEE Transactions on Pattern Analysis and Machine Intelligence. – 1998 - 20 (8): 832–844.
Language: English
Submitted on: Jun 10, 2024
Published on: Mar 27, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2025 Ali Sajae Mannaa, Tatiana A Makarenya, Alexey I Kalinichenko, Svetlana V Petrenko, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.