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Digital Library of the
European Council for Modelling and Simulation |
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Title: |
Methodology For Elliott Waves
Pattern Recognition |
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Authors: |
Martin Kotyrba, Eva Volna, Michal Janosek, Hashim Habiballa, David Brazina |
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Published in:
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(2013).ECMS 2013 Proceedings edited
by: W. Rekdalsbakken, R. T. Bye, H. Zhang European Council for Modeling
and Simulation. doi:10.7148/2013 ISBN:
978-0-9564944-6-7 27th
European Conference on Modelling and Simulation, Aalesund, Norway, May 27th –
30th, 2013 |
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Citation
format: |
Martin Kotyrba,
Eva Volna, Michal Janosek,
Hashim Habiballa, David Brazina (2013). Methodology For Elliott Waves Pattern
Recognition, ECMS 2013 Proceedings edited by: W. Rekdalsbakken, R. T. Bye,
H. Zhang, European Council for Modeling and Simulation. doi:10.7148/2013-0349 |
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DOI: |
http://dx.doi.org/10.7148/2013-0349 |
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Abstract: |
The article is focused on an analysis and pattern recognition in time series, which
are fractal in nature. The proposal methodology is based on an interdisciplinary
approach that combines artificial neural networks, analytic programming,
Elliott wave theory and knowledge modelling. The
heart of the methodology are a methods, which is able to recognize Elliott
waves structures including their deformation in the charts and helps to more
efficient prediction of its trend. The functionality of the proposed
methodology was validated in experimental simulations, for whose implementation
was designed and created an application environment. Experimental simulations
have shown that the method is usable to a wider class of problems than the
theory itself allows only Elliott waves. This paper introduces a methodology
that allows analysis of Elliot wave’s patterns in time series for the purpose
of a trend prediction. |
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