Построение финансовых временных рядов с применением нейронных сетей в отношении прогнозных показателей предприятия
Журнал «KANT» №1(54) 2025 [стр. 51-62]
DOI: 10.24923/2222-243X.2025-54.9
Ключевые слова: прогнозирование финансовых показателей производства; искусственные нейронные сети; прогнозирование временных рядов; средняя абсолютная процентная ошибка.
Цитировать: Захматов Д.Ю., Киршин И.А., Кох И.А. Построение финансовых временных рядов с применением нейронных сетей в отношении прогнозных показателей предприятия // KANT. – 2025. – №1(54). – С. 51-62. EDN: CBQVLE. DOI: 10.24923/2222-243X.2025-54.9
Поиск и тестирование новых методов прогнозирования финансовых показателей производства является актуальной задачей экономической науки и практики рационализации финансово-хозяйственной деятельности. В статье анализируется применение технологий искусственного интеллекта, в частности искусственных нейронных сетей, для повышения точности прогнозирования объемов производства. Обучение и тестирование нейросетей происходило на данных ЕМИСС по отечественному производству лесоматериалов, необработанных с 01.01.2010 по 12.01.2024 гг. В статье обосновывается эффективность нейросетевого моделирования в программной среде Statistica, обеспечивающей достаточную точность прогнозирования, оцененную средней абсолютной процентной ошибкой. Авторами была определена искусственная нейронная сеть, параметры которой оценивались с помощью контролируемого процесса обучения с прямой связью. Полученные результаты прогнозирования одного из финансовых показателей производства – величины выпускаемой продукции, определяют возможности применения моделей искусственного интеллекта для разработки планов развития производства и процессов принятия стратегических решений.
Литература:
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28. Orhan U., Hekim M., Ozer M. EEG signals classification using the K-means clustering and a multilayer perceptron neural network model // Expert Syst. 2011, 38, 13475-13481.
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33. Sezer Omer Berat, Mehmet Ugur Gudelek, Ahmet Murat Ozbayoglu. Financial time series forecasting with deep learning: A systematic literature review: 2005-2019. // Applied soft computing. 2020. 90:106181. https://doi.org/10.48550/arXiv.1911.13288
34. Sharda R., Patil R.B. Connectionist approach to time series prediction: An empirical test // Journal of Intelligent Manufacturing. 1992. No 3. Pp. 317-323.
35. Tang Z., Fishwick P.A. Feedforward neural nets as models for time series forecasting // ORSA Journal on Computing. 1993. No 5(4), Pp. 374-385.
36. U. Anders, Korn O. Model selection in neural networks // Neural Networks. 2000. Vol. 12, Pp. 309-323.
37. Y. Deshmukh. Stock Market Prediction using Neural Network and MLP // International Journal of Innovative Science, Engineering & Technology. 2020. Vol. 7, no. 2.
38. ЕМИСС. Официальные статистические показатели. Производство основных видов продукции в натуральном выражении (оперативные данные в соответствии с ОКПД2). https://fedstat.ru/indicators/
1. AlFuhaid S., El-Sayed M.A., Mahmoud M.S. Cascaded artificial neural networks for short-term load forecasting // IEEE Trans. Power Systems. 1997. Vol. 12, no. 4. Pp. 1524-1529.
2. Abhiram Dash, Debasis Bhattacharya, Digvijay Singh Dhakre, Madhuchhanda Kishan. On Selection of Best Model from Spline Regression and ARIMA Models for Forecasting Rabi Food Grain Production of Odisha // Environment and Ecology. – 2022. – №40(3B). Рр. 1428-1437.
3. Aditi Mittal. Understanding RNN and LSTM [Электронный ресурс]. URL: https://towardsdatascience.com/understanding-rnn-and-lstm-f7cdf6dfc14e
4. Akyuz Ilker. Future projection and the sales of industrial wood in Turkey: artificial neural networks // Turkish Journal of Agriculture and Forestry. 2019. Vol. 43, №3, Article 11. Рр. 368-377.
5. Benidis K., Rangapuram S.S. Deep learning for time series forecasting: Tutorial and literature survey // ACM Computing Surveys. Vol. 55, Issue 6, Article 121, Pp. 1-36. https://doi.org/10.1145/3533382
6. Fargana Abdullayeva, Yadigar Imamverdiy-ev. Development of Oil Production Forecasting Method based on Deep Learning // Statistics, optimization and information computing. 2019. Vol. 7, Pp 826-839. DOI: 10.19139/soic-2310-5070-651.
7. Ferasso M., Alnoor A. Artificial Neural Network and Structural Equation Modeling in the Future // In Artificial neural networks and structural equation modeling. Berlin/Heidelberg: Springer, 2022. Pp. 327-341.
8. Darbellay G.A., Slama M. Forecasting the short-term demand for electricity. Do neural networks stand a better chance? // Int. J. Forecast. 2000. Vol. 16, Pp. 71-83.
9. Germania Vayas-Ortega, Cristina Soguero-Ruiz, Margarita Rodriguez-Ibanez, Jose-Luis Rojo-Alvarez and Francisco-Javier Gimeno-Blanes. On the Differential Analysis of Enterprise Valuation Methods as a Guideline for Unlisted Companies Assessment (II) // Applying Machine-Learning Techniques for Unbiased Enterprise Value Assessment. Appl. Sci. 2020, 10, 5334; doi:10.3390/app10155334
10. Gungцr I, Kayacan MC, Korkmaz M. Artificial neural networks use in the forecasting of industrial wood demand and comparison with different estimation methods // In: ORIE XXIV. National Congress 15-18 June; Adana, Turkey: Зukurova University. 2004.
11. Guoqiang Zhang, Patuwo B. Eddy, Hu Michael Y. Forecasting with artificial neural networks: The state of the art // International Journal of Forecasting. 1998. Vol. 14, Is. 1, Pp. 35-62. https://doi.org/10.1016/S0169-2070(97)00044-7
12. Hippert H.S., Pedreira C.E. and Souza R.C. Neural networks for short-term load forecasting: a review and evaluation // In IEEE Transactions on Power Systems. 2001. Vol. 16, no. 1, Pp. 44-55, doi: 10.1109/59.910780.
13. Hazarika N., Chen J.Z., Tsoi A., Sergejew A. Classification of EEG signals using the wavelet transform // Signal Process. 1997.
14. Hossein Abbasimehr, Mostafa Shabani, Mohsen Yousefi. An optimized model using LSTM network for demand forecasting // Computers & Industrial Engineering. 2020. Vol. 143, 106435. https://doi.org/10.1016/j.cie.2020.106435
15. Huang D., Wu Z. Forecasting Outpatient Visits Using Empirical Model Decomposition Coupled With Back Propagation Artificial Neural Networks Optimized by Particle Swarm Optimization // PLoS ONE. 2017. №12(2). Pp. 1-17.
16. Drezga and S. Rahman. Short-term load forecasting with local ANN predictors // IEEE Trans. Power Systems. 1999. Vol. 14, no. 3. Pp. 844-850.
17. L. Ho, M. Xie. The use of ARIMA models for reliability forecasting and analysis // Computers & Industrial Engineering. 1998, Vol. 35, Is. 1-2, Pp. 213-216. https://doi.org/10.1016/S0360-8352(98)00066-7.
18. Lapedes A., Farber R. How neural nets work // Neural Information Processing Systems, American Institute of Physics. New York, 1988. Pp. 442-456.
19. Lee S.; Choeh J.Y. Predicting the helpfulness of online reviews using multilayer perceptron neural networks // Expert Syst. Appl. 2014, 41. Pp. 3041-3046.
20. M. Adya and F. Collopy. How effective are neural networks at forecasting and prediction? A review and evaluation // J. Forecast. 1998. Vol. 17, Pp. 481-495.
21. Mateus B.C., Mendes M., Farinha J.T., Cardoso A.J.M., Assis R., da Costa L.M. Forecasting Steel Production in the World // Assessments Based on Shallow and Deep Neural Networks. Appl. Sci. 2023, 13, 178. https://doi.org/10.3390/ app13010178
22. Michael I. Jordan. Serial order: A parallel, distributed processing approach. Technical report, Institute for Cognitive Science, University of California, San Diego, 1986.
23. Michael I. Jordan. Serial order: A parallel, distributed processing approach. In Advances in Connectionist Theory: Speech. Erlbaum, 1989.
24. Mishra P., Sahu P.K., Padmanaban K., Vishwajith K.P., Dhekale B.S. Study of instability and forecasting of food grain production in India, 2015.
25. Negash B.M., Yaw A.D. Artificial neural network based production forecasting for a hydrocarbon reservoir under water injection // Petroleum Exploration and Development. 2020. 47(2): 383-392. DOI:10.1016/S1876-3804(20)60055-6
26. Nouf Alkaabi, Siddhartha Shakya. Comparing ML Models for Food Production Forecasting, 2022.
27. Ogulata S.N., Sahin C., Erol R. Neural network-based computer-aided diagnosis in classification of primary generalized epilepsy by EEG signals // J. Med. Syst. 2009, 33, 107-112.
28. Orhan U., Hekim M., Ozer M. EEG signals classification using the K-means clustering and a multilayer perceptron neural network model // Expert Syst. 2011, 38, 13475-13481.
29. P. Mishra, Sahu P.K., Devi M., Fatih Ch. and Williams A.J. Forecasting of Rice Production using the Meteorological Factor in Major States in India and its Role in Food Security. 2021. Paper No. 881.
30. Petropoulos F., Makridakis S., Assimakopoulos V., Nikolopoulos K. 'Horses for courses' in demand forecasting // Eur J Oper Res. 2014. No 237. Pp. 152-63.
31. R.B.C. Gharbi and G.A. Mansoori. An introduction to artificial intelligence applications in petroleum exploration and production. J. Petrol // Sci.& Eng'g. 2005. 49(3-4): 93-96, 2005. DOI: 10.1016/j.petrol.2005.09.001.
32. Salima A. Bilhassan, Abdelsalam El jameli, Ahmed El majbri and Abdelaziz Badi. Forecasting of Production Using Artificial Intelligence // IOSR Journal of Engineering (IOSRJEN). 2024. Vol. 14, Issue 9, Pp. 24-27.
33. Sezer Omer Berat, Mehmet Ugur Gudelek, Ahmet Murat Ozbayoglu. Financial time series forecasting with deep learning: A systematic literature review: 2005-2019. // Applied soft computing. 2020. 90:106181. https://doi.org/10.48550/arXiv.1911.13288
34. Sharda R., Patil R.B. Connectionist approach to time series prediction: An empirical test // Journal of Intelligent Manufacturing. 1992. No 3. Pp. 317-323.
35. Tang Z., Fishwick P.A. Feedforward neural nets as models for time series forecasting // ORSA Journal on Computing. 1993. No 5(4), Pp. 374-385.
36. U. Anders, Korn O. Model selection in neural networks // Neural Networks. 2000. Vol. 12, Pp. 309-323.
37. Y. Deshmukh. Stock Market Prediction using Neural Network and MLP // International Journal of Innovative Science, Engineering & Technology. 2020. Vol. 7, no. 2.
38. ЕМИСС. Официальные статистические показатели. Производство основных видов продукции в натуральном выражении (оперативные данные в соответствии с ОКПД2). https://fedstat.ru/indicators/
Construction of financial time series using neural networks in relation to enterprise forecast indicators
Keywords: forecasting financial indicators of production; artificial neural networks; time series forecasting; average absolute percentage error.
Searching for and testing new methods for forecasting financial indicators of production is an urgent task of economic science and practice of rationalization of financial and economic activities. The article analyzes the use of artificial intelligence technologies, in particular artificial neural networks, to improve the accuracy of forecasting production volumes. Training and testing of neural networks was carried out on the EMISS data on domestic production of unprocessed timber from 01.01.2010 to 12.01.2024. The article substantiates the effectiveness of neural network modeling in the Statistica software environment, which provides sufficient forecasting accuracy, estimated by the average absolute percentage error. The authors defined an artificial neural network, the parameters of which were estimated using a supervised learning process with a feedforward connection. The obtained results of forecasting one of the financial indicators of production – the volume of manufactured products, determine the possibilities of using artificial intelligence models to develop production development plans and strategic decision-making processes.