Publicación:
A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting

Vista sencilla de ítem
dc.contributor.authorLazcano, Ana
dc.contributor.authorHerrera Caro, Pedro Javier
dc.contributor.authorMonge, Manuel
dc.date.accessioned2025-04-24T09:46:58Z
dc.date.available2025-04-24T09:46:58Z
dc.date.issued2023-01-02
dc.descriptionThe registered version of this article, first published in “Mathematics, vol. 11, 2023", is available online at the publisher's website: MDPI, https://doi.org/10.3390/math11010224 La versión registrada de este artículo, publicado por primera vez en “Mathematics, vol. 11, 2023", está disponible en línea en el sitio web del editor: MDPI, https://doi.org/10.3390/math11010224
dc.description.abstractAccurate and real-time forecasting of the price of oil plays an important role in the world economy. Research interest in forecasting this type of time series has increased considerably in recent decades, since, due to the characteristics of the time series, it was a complicated task with inaccurate results. Concretely, deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have appeared in this field with promising results compared to traditional approaches. To improve the performance of existing networks in time series forecasting, in this work two types of neural networks are brought together, combining the characteristics of a Graph Convolutional Network (GCN) and a Bidirectional Long Short-Term Memory (BiLSTM) network. This is a novel evolution that improves existing results in the literature and provides new possibilities in the analysis of time series. The results confirm a better performance of the combined BiLSTM-GCN approach compared to the BiLSTM and GCN models separately, as well as to the traditional models, with a lower error in all the error metrics used: the Root Mean Squared Error (RMSE), the Mean Squared Error (MSE), the Mean Absolute Percentage Error (MAPE) and the R-squared (R2). These results represent a smaller difference between the result returned by the model and the real value and, therefore, a greater precision in the predictions of this model.en
dc.description.versionversión publicada
dc.identifier.citationLazcano, A., Herrera, P. J., & Monge, M. (2023). A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting. Mathematics, 11(1), 224. https://doi.org/10.3390/math11010224
dc.identifier.doihttps://doi.org/10.3390/math11010224
dc.identifier.issn2227-7390
dc.identifier.urihttps://hdl.handle.net/20.500.14468/26494
dc.journal.issue1
dc.journal.titleMathematics
dc.journal.volume11
dc.language.isoen
dc.page.initial224
dc.publisherMDPI
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentIngeniería de Software y Sistemas Informáticos
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordstime series forecastingen
dc.subject.keywordsfinancial forecastingen
dc.subject.keywordsrecurrent neural networken
dc.subject.keywordsBiLSTMen
dc.subject.keywordsgraph convolutional networken
dc.titleA Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecastingen
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublicationd8a786c8-b4c1-42a1-8cdd-e7e0480024e0
relation.isAuthorOfPublication.latestForDiscoveryd8a786c8-b4c1-42a1-8cdd-e7e0480024e0
Archivos
Bloque original
Mostrando 1 - 1 de 1
Miniatura
Nombre:
HerreraCaro_PedroJavier_Combined_Model.pdf
Tamaño:
4.39 MB
Formato:
Adobe Portable Document Format
Descargar
Bloque de licencias
Mostrando 1 - 1 de 1
No hay miniatura disponible
Nombre:
license.txt
Tamaño:
3.62 KB
Formato:
Item-specific license agreed to upon submission
Descripción:
Descargar
Colecciones
Artículos y papers