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Authors: Nijolė Maknickienė and Algirdas Maknickas

Affiliation: Vilnius Gediminas Technical University, Lithuania

Keyword(s): Prediction, EVOLINO, Financial Markets, Recurrent Neural Networks Ensembles.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Complex Artificial Neural Network Based Systems and Dynamics ; Computational Intelligence ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Multi-Agent Intelligent Systems and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: Modern portfolio theory of investment-based financial market forecasting use probability distributions. This investigation used a neural network architecture, which allows to obtain distribution for predictions. Comparison of the two different models - points based prediction and distributions based prediction - opens new investment opportunities. Dependence of forecasting accuracy on the number of EVOLINO recurrent neural networks (RNN) ensemble was obtained for five forecasting points ahead. This study allows to optimize the computational time and resources required for sufficiently accurate prediction.

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Paper citation in several formats:
Maknickienė, N. and Maknickas, A. (2013). Investigation of Prediction Capabilities using RNN Ensembles. In Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA; ISBN 978-989-8565-77-8; ISSN 2184-3236, SciTePress, pages 391-395. DOI: 10.5220/0004554703910395

@conference{ncta13,
author={Nijolė Maknickienė. and Algirdas Maknickas.},
title={Investigation of Prediction Capabilities using RNN Ensembles},
booktitle={Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA},
year={2013},
pages={391-395},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004554703910395},
isbn={978-989-8565-77-8},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 5th International Joint Conference on Computational Intelligence (IJCCI 2013) - NCTA
TI - Investigation of Prediction Capabilities using RNN Ensembles
SN - 978-989-8565-77-8
IS - 2184-3236
AU - Maknickienė, N.
AU - Maknickas, A.
PY - 2013
SP - 391
EP - 395
DO - 10.5220/0004554703910395
PB - SciTePress

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