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Knowledge discovery in dynamic data using neural networks

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Abstract

The paper proposes a new approach to implement common neural network algorithms in the network environment. In our experimental study we have used three different types of neural networks based on Hebb, daline and backpropagation training rules. Our goal was to discover important market (Forex) patterns which repeatedly appear in the market history. Developed classifiers based upon neural networks should effectively look for the key characteristics of the patterns in dynamic data. We focus on reliability of recognition made by the described algorithms with optimized training patterns based on the reduction of the calculation costs. To interpret the data from the analysis we created a basic trading system and trade all recommendations provided by the neural network.

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Acknowledgments

The research described here has been financially supported by University of Ostrava Grant SGS/PřF/2015 and by the European Regional Development Fund in the IT4 Innovations Centre of Excellence Project (CZ.1.05/1.1.00/02.0070).

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Authors and Affiliations

  1. Department of Informatics and Computers, University of Ostrava, Dvořákova 7, 702 00, Ostrava, Czech Republic

    Michal Janosek, Eva Volna & Martin Kotyrba

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  1. Michal Janosek
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  2. Eva Volna
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  3. Martin Kotyrba
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Correspondence to Martin Kotyrba.

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Janosek, M., Volna, E. & Kotyrba, M. Knowledge discovery in dynamic data using neural networks. Cluster Comput 18, 1411–1421 (2015). https://doi.org/10.1007/s10586-015-0491-3

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