dc.contributor.author | Maknickienė, Nijolė | |
dc.contributor.author | Maknickas, Algirdas | |
dc.date.accessioned | 2023-09-18T19:53:22Z | |
dc.date.available | 2023-09-18T19:53:22Z | |
dc.date.issued | 2013 | |
dc.identifier.other | (BIS)VGT02-000027041 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/144467 | |
dc.description.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. Com- parison 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. | eng |
dc.format.extent | p. 391-395 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | INSPEC | |
dc.source.uri | http://www.ijcci.org/ | |
dc.title | Investigation of prediction capabilities using RNN ensembles | |
dc.type | Straipsnis konferencijos darbų leidinyje kitoje DB / Paper in conference publication in other DB | |
dcterms.references | 23 | |
dc.type.pubtype | P1c - Straipsnis konferencijos darbų leidinyje kitoje DB / Article in conference proceedings in other DB | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Verslo vadybos fakultetas / Faculty of Business Management | |
dc.contributor.faculty | Fundamentinių mokslų fakultetas / Faculty of Fundamental Sciences | |
dc.subject.researchfield | S 004 - Ekonomika / Economics | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.en | Prediction | |
dc.subject.en | EVOLINO | |
dc.subject.en | Financial Markets | |
dc.subject.en | Recurrent Neural Networks Ensembles | |
dcterms.sourcetitle | IJCCI 2013 : 5th International Joint Conference on Computational Intelligence, 20th to 22nd September, 2013, Vilamoura, Portugal / Institute for Systems and Technologies of Information, Control and Communication | |
dc.publisher.name | INSTICC | |
dc.publisher.city | Setubal | |
dc.identifier.elaba | 4039420 | |