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Prediction Capabilities of Evolino RNN Ensembles

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Computational Intelligence (IJCCI 2013)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 613))

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Abstract

Modern portfolio theory of investment-based financial market forecasting use probability distributions. This investigation used an ensemble of genetic algorithm based recurrent neural networks (RNN), which allows to obtain multi-modal distribution for predictions. Comparison of the two different models—scatted points based prediction and distributions based prediction—opens new opportunities to create profitable investment tool, which was tested in real time demo market. Dependence of forecasting accuracy on the number of Evolino recurrent neural networks ensemble was obtained for five forecasting points ahead. This study allows to optimize the cluster based computational time and resources required for sufficiently accurate prediction.

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Authors and Affiliations

  1. Vilnius Gediminas Technical University, Sauletekio Al. 11, 10223, Vilnius, Lithuania

    Nijolė Maknickienė & Algirdas Maknickas

Authors
  1. Nijolė Maknickienė
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  2. Algirdas Maknickas
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Correspondence to Nijolė Maknickienė .

Editor information

Editors and Affiliations

  1. Images, Signals and Intelligence Systems Laboratory, University PARIS-EST Creteil (UPEC), Créteil, France

    Kurosh Madani

  2. Departamento de Engenharia Informatica Polo II - Pinhal de Marrocos, University of Coimbra, Coimbra, Portugal

    António Dourado

  3. aSEEB-ISR-IST Av. Rovisco Pais, 1, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  4. Rua do Vale de Chaves, Estefanilha, Polytechnic Institute of Setúbal / INSTICC, Setúbal, Portugal

    Joaquim Filipe

  5. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Maknickienė, N., Maknickas, A. (2016). Prediction Capabilities of Evolino RNN Ensembles. In: Madani, K., Dourado, A., Rosa, A., Filipe, J., Kacprzyk, J. (eds) Computational Intelligence. IJCCI 2013. Studies in Computational Intelligence, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-23392-5_26

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  • DOI: https://doi.org/10.1007/978-3-319-23392-5_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23391-8

  • Online ISBN: 978-3-319-23392-5

  • eBook Packages: EngineeringEngineering (R0)

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