INFORMATION TECHNOLOGY OF STOCK INDEXES FORECASTING ON THE BASE OF FUZZY NEURAL NETWORKS

Yuriy TRYUS; Nataliya  ANTIPOVA; Kateryna ZHURAVEL; Grygoriy ZASPA

doi:10.23743/acs-2017-03

Open full text

pdf

Published: Mar 30, 2017

DOI: https://doi.org/10.23743/acs-2017-03

Issue Vol. 13 No. 1 (2017)

Articles

AERODYNAMIC RESEARCH OF THE OVERPRESSURE DEVICE FOR INDIVIDUAL TRANSPORT
Paweł MAGRYTA

5-19
MODELLING OF A LARGE ROTARY HEAT EXCHANGER
Tytus TULWIN

20-28
INFORMATION TECHNOLOGY OF STOCK INDEXES FORECASTING ON THE BASE OF FUZZY NEURAL NETWORKS
Yuriy TRYUS, Nataliya ANTIPOVA, Kateryna ZHURAVEL, Grygoriy ZASPA

29-40
CONSTRUCTION AND TECHNOLOGICAL ANALYSIS OF THE BROACH BLADE SHAPE USING THE FINITE ELEMENT METHOD
Stanisław BŁAWUCKI, Kazimierz ZALESKI

41-50
CRANK-PISTON MODEL OF INTERNAL COMBUSTION ENGINE USING CAD/CAM/CAE IN THE MSC ADAMS
Michał BIAŁY, Marcin SZLACHETKA

51-60
FIREWORKS ALGORITHM FOR UNCONSTRAINED FUNCTION OPTIMIZATION PROBLEMS
Evans BAIDOO

61-74
USEFULNESS OF MODAL ANALYSIS FOR EVALUATION OF MILLING PROCESS STABILITY
Paweł PIEŚKO, Magdalena ZAWADA-MICHAŁOWSKA

75-84
SURVEY OF REMOTELY CONTROLLED LABORATORIES FOR RESEARCH AND EDUCATION
Tomasz CHMIELEWSKI, Katarzyna ZIELIŃSKA

85-96

DOI

https://doi.org/10.23743/acs-2017-03

Authors

Yuriy TRYUS

tryusyv@gmail.com

Computer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy, Ukraine

Nataliya ANTIPOVA

LesleyRoot1726@gmail.com

Computer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy,, Ukraine

Kateryna ZHURAVEL

k_galasun@mail.ru

Computer Science and Information Technology Department, Cherkasy State Technological University,460 Shevchenko Blvd, 18006, Cherkasy,, Ukraine

Grygoriy ZASPA

gzaspa@ukr.net

Software Department, Cherkasy State Technological University, 460 Shevchenko Blvd, 18006, Cherkasy, Ukraine

Abstract

In this research the information technology for stock indexes forecast on the base of fuzzy neural networks was created. Thepossibility of its use for multi-parameter short-time stock indexes forecasts, in particular S&P500, DJ, NASDAC was checked. Thecreated information technology is used making several consequential steps. The stock indexes forecast numeral experiment based on real data for period of several years with use of the technology offered was made.

Keywords:

neural networks, fuzzy neural networks, forecasting, stock indexes

References

Adaptive Neuro-Fuzzy Modeling. (n.d.). Retrieved September 26, 2016, from MathWorks website, https://www.mathworks.com/help/fuzzy/adaptive-neuro-fuzzy-inference-systems.html

Adaptive neuro-fuzzy inference system. (n.d.). Retrieved September 26, 2016, from MathWorks website, https://www.mathworks.com/help/fuzzy/neuro-adaptive-learning-and-anfis.html

Jang, J.-S. R., (1993). ANFIS: Adaptive-Network-based Fuzzy Inference Systems. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. DOI: https://doi.org/10.1109/21.256541

Jang, J.-S. R., & Sun, C.-T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378–406. DOI: https://doi.org/10.1109/5.364486

Jang, J.-S. R., & Sun, C.-T. (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Upper Saddle River, NJ: Prentice Hall. DOI: https://doi.org/10.1109/TAC.1997.633847

Mohaddes, S. A., & Fahimifard, S. M. (2015). Application of Adaptive Neuro-Fuzzy Inference System (ANFIS) in Forecasting Agricultural Products Export Revenues (Case of Iran’s Agriculture Sector). Journal of Agricultural Science and Technology, 17(1), 1–10.

Svalina, I., Galzina, V., Lujić, R., & Šimunović, G. (2013). An adaptive network-based fuzzy inference system (ANFIS) for the forecasting: The case of close price indices. Expert Systems with Applications, 40(15), 6055-6063. https://doi.org/10.1016/j.eswa.2013.05.029 DOI: https://doi.org/10.1016/j.eswa.2013.05.029

Toolbox fuzzy-logic Matlab. (n.d.). Retrieved September 28, 2016, from MathWorks website, http://www.mathworks.com/products/fuzzy-logic/

Wang, L.-X. (1994). Adaptive fuzzy systems and control: design and stability analysis. Upper Saddle River, NJ: Prentice Hall.

Wang, Y. M., & Elhag, T. (2008). An Adaptive Neuro-fuzzy Inference System for Bridge Risk Assessment. Expert Systems with Applications, 34(4), 3099–3106. https://doi.org/10.1016/j.eswa.2007.06.026 DOI: https://doi.org/10.1016/j.eswa.2007.06.026

YahooFinance – BusinessFinance, StockMarket, Quotes, News. (n.d.). Retrieved September 21, 2016, from YahooFinance website, http://finance.yahoo.com

Zhang, G., & Hu, M. Y. (1998). Neural Network Forecasting of the British Pound/US Dollar Exchange Rate. Omega The International Journal of Management Science, 26(4), 495–506. https://doi.org/10.1016/S0305-0483(98)00003-6 DOI: https://doi.org/10.1016/S0305-0483(98)00003-6

TRYUS, Y., ANTIPOVA, N. ., ZHURAVEL, K., & ZASPA, G. (2017). INFORMATION TECHNOLOGY OF STOCK INDEXES FORECASTING ON THE BASE OF FUZZY NEURAL NETWORKS. Applied Computer Science, 13(1), 29–40. https://doi.org/10.23743/acs-2017-03

INFORMATION TECHNOLOGY OF STOCK INDEXES FORECASTING ON THE BASE OF FUZZY NEURAL NETWORKS

Issue Vol. 13 No. 1 (2017)

Archives

DOI

Authors

Abstract

Keywords:

References

License

Article Sidebar

Issue Vol. 13 No. 1 (2017)

Archives

Main Article Content

DOI

Authors

Abstract

Keywords:

References

Article Details

License