MARČEK, Dušan. FORECASTING FINANCIAL DATA: A COMBINED MODEL OF FUZZY NEURAL NETWORK AND STATISTICS. Online. In ZENG, Xianyi; LU, Jie; KERRE, Etienne E.; MARTINEZ, Luis; KOEHL, Ludovic. Uncertainty Modelling in Knowledge Engineering and Decision Making: Proceedings of the 12th International FLINS Conference (FLINS 2016). Volume 10. Singapore: World Scientific Publishing, 2016, p. 1137-1142. ISBN 978-981-314-698-3. Available from: https://dx.doi.org/10.1142/9789813146976_0175.
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Basic information
Original name FORECASTING FINANCIAL DATA: A COMBINED MODEL OF FUZZY NEURAL NETWORK AND STATISTICS
Authors MARČEK, Dušan (703 Slovakia, guarantor, belonging to the institution).
Edition Volume 10. Singapore, Uncertainty Modelling in Knowledge Engineering and Decision Making: Proceedings of the 12th International FLINS Conference (FLINS 2016), p. 1137-1142, 6 pp. 2016.
Publisher World Scientific Publishing
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 20205 Automation and control systems
Country of publisher Singapore
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/47813059:19240/16:A0000126
Organization unit Faculty of Philosophy and Science in Opava
ISBN 978-981-314-698-3
Doi http://dx.doi.org/10.1142/9789813146976_0175
UT WoS 000417158200175
Keywords in English Financial Data Forecasting; Fuzzy neural network; Statistical modelling; Combined model
Tags ÚI
Tags International impact, Reviewed
Links LQ1602, research and development project.
Changed by Changed by: Mgr. Kamil Matula, Ph.D., učo 7389. Changed: 7/1/2020 11:14.
Abstract
In this paper, we apply the ARMA/ARCH methodology to develop forecasting models and compare their forecast accuracy with a class of novel hybrid fuzzy logic RBF neural network models. The used novel approach deals with nonlinear estimate of various RBF NN-based ARMA/GARCH methodologies. Our results show that the proposed approach achieves better forecast accuracy on the validation dataset than most available techniques.
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