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Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/588

Title: Modelling of the History and Predictions of Financial Market Time Series Using Evolino
Authors: Rutkauskas, Aleksandras Vytautas
Maknickienė, Nijolė
Maknickas, Algirdas
Keywords: Evolino
recurrent neural networks
prediction of markets
chaos modelling
orthogonality
Issue Date: 2010
Publisher: Vilniaus Gedimino technikos universitetas
Citation: Rutkauskas, A. V.; Maknickienė, N.; Maknickas, A. 2010. Modelling of the history and predictions of financial market time series using Evolino, in The 6th International Conference “Business and Management 2010”: Selected papers, Vol. 1. Ed. by R. Ginevičius, A. V. Rutkauskas, R. Počs, May 13–14, 2010, Vilnius, Lithuania. Vilnius: Technika, 170–175. doi:10.3846/bm.2010.024
Abstract: Artificial neural networks and their systems are already capable of learning, to summarize, filter, and classify information. The increasing amount of authors are trying to teach them to approximate and predict chaotic, fractal processes. One of the greatest challenges of today's financial researches is forecasting of the commodities, stocks and currency markets. Variations in prices lead to economic indicators as result of investment process of investors and short time market players. Present article investigates recurrent neural network systems as mathematical tool for objective forecasts of fractal behaviour of financial markets by Evolino recurrent neural network learning algorithm.
URI: http://dspace1.vgtu.lt/handle/1/588
ISSN: 2029-4441
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