Investigation of Prediction Capabilities using RNN Ensembles

Nijolė Maknickienė, Algirdas Maknickas

2013

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 Harvard Style

Maknickienė N. and Maknickas A. (2013). Investigation of Prediction Capabilities using RNN Ensembles . In Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8, pages 391-395. DOI: 10.5220/0004554703910395


in Bibtex Style

@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 - Volume 1: NCTA, (IJCCI 2013)},
year={2013},
pages={391-395},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004554703910395},
isbn={978-989-8565-77-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - Volume 1: NCTA, (IJCCI 2013)
TI - Investigation of Prediction Capabilities using RNN Ensembles
SN - 978-989-8565-77-8
AU - Maknickienė N.
AU - Maknickas A.
PY - 2013
SP - 391
EP - 395
DO - 10.5220/0004554703910395