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2016 | OriginalPaper | Buchkapitel

Prediction Capabilities of Evolino RNN Ensembles

verfasst von : Nijolė Maknickienė, Algirdas Maknickas

Erschienen in: Computational Intelligence

Verlag: Springer International Publishing

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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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Metadaten
Titel
Prediction Capabilities of Evolino RNN Ensembles
verfasst von
Nijolė Maknickienė
Algirdas Maknickas
Copyright-Jahr
2016
Verlag
Springer International Publishing
DOI
https://doi.org/10.1007/978-3-319-23392-5_26