Papers Papers/2020



Paper Unlock

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.


Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - NCTA, (IJCCI 2013) ISBN 978-989-8565-77-8; ISSN 2184-2825, pages 391-395. DOI: 10.5220/0004554703910395

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 - NCTA, (IJCCI 2013)},


JO - Proceedings of the 5th International Joint Conference on Computational Intelligence - NCTA, (IJCCI 2013)
TI - Investigation of Prediction Capabilities using RNN Ensembles
SN - 978-989-8565-77-8
IS - 2184-2825
AU - Maknickienė, N.
AU - Maknickas, A.
PY - 2013
SP - 391
EP - 395
DO - 10.5220/0004554703910395