Forecasting time series of financial indicators using a multilayer neural network.
Keywords:
multilayer neural network, sales volume forecasting, forecasting information system
Abstract
The work is devoted to modeling a multilayer neural network for forecasting sales volumes of product groups. An analysis of publications on the use of neural networks in the field of forecasting financial indicators is presented. The developed software for modeling a multilayer neural network and further forecasting is described. Examples of time series analysis and the formation of a forecast based on it using a two-layer neural network are given.
References
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2. Mnykh E.V. Economic analysis: in hand. / Kyiv: Znannia, 2011. 630 p.
3. Bachevskyi B. E. The potential and development of the enterprise: education. manual / Kyiv: Center for Educational Literature, 2009. 400 p.
4. Subbotin S. O. Neural networks: theory and practice: teaching. guide.– Zhytomyr: Ed. O.O. Evenok, 2020. - 184 p.
5. Tymoshchuk P. V. Artificial neural networks Study guide.// Lviv: Publishing House of Lviv Polytechnic. - 2011. - 444 p.
6. Lyeonov S., Kuzmenko O., Bozhenko V., Mursalov M., Zeynalov Z., Huseynova A. Forecasting the risk of money laundering through financial intermediaries. Financial and Credit Activity Problems of Theory and Practice, 4(35), 2021. pp. 191–201.
7. Lukyanova V., Matsiuk N. Comparison of neural networks and time-seriesdecomposition method for sales forecasting trading company. Scientific Bulletin of Chernivtsi University. 2012. No.623-626. Pp. С. 332-338.
8. Gorchakova I. A., Vorvarovskaya N. N. Forecasting the price index in the real estate market using the ARIMA model and neural networks. Bulletin of Khmelnitsky National University. 2010. №4. pp. 74–86.
9. Levitskaya T.A., Zhelezov D.A. Modeling neural networks for assessing the market value of a residential property. Computer-integrated technologies: education, science, production. 2020. No 41. pp. 59-64
10. Galeshchuk S. Pieces of neural networks in forecasting the currency market. Bulletin of the Kiev National Trade and Economic University. 2016. Issue No. 3. P. 101–114
11. Levitskaya T.A., Romanov K.G. Modelling stock markets forecasting using neural networks. No. 35 2017: Reporter of the Priazovskyi State Technical University. Section: Technical sciences (2017). No 35. Pp 226-230
12. Gadetska Z. Assessment of risk of bankruptcy of banks of ukraine by a modern method of artificial neural networks. Economy and Society, 2019. No 20. pp. 753-761.
13. Kovshik V.I. Selection of piece-wise neural networks for predicting the logistics of machine business enterprises. Skhidna Evropa: economy, business and management. – Dnipro: DVNZ “Pridniprovska State Academy of Life and Architecture. 2016. No. 4. pp. 430–435.
14. Stavitsky O. V., Mozolevska M. O. Using neural for forecasting in the financial sector. Actual problems of economy and management. 2017. No. 19. pp. 35–42.
15. Mnushko Z. M., Popova Yu. V., Timanyuk I. V. Multi-layer artificial neural network in predicting the capacity of the pharmaceutical market. Clinical informatics and telemedicine./ 2009. Т. 5, No 6. pp. 69-74.
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Published
2022-12-18
How to Cite
Levitskaya , T., & Herasymov , D. (2022). Forecasting time series of financial indicators using a multilayer neural network. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (49), 48-53. https://doi.org/10.36910/6775-2524-0560-2022-49-07
Section
Computer science and computer engineering
