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Prediction Capabilities of Evolino RNN Ensembles

  • Nijolė MaknickienėEmail author
  • Algirdas Maknickas
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 613)

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.

Keywords

Distribution of expected returns Ensembles Evolino Financial markets Prediction Recurrent neural networks 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Vilnius Gediminas Technical UniversityVilniusLithuania

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