Publicación: A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting
Cargando...
Fecha
2023-01-02
Editor/a
Director/a
Tutor/a
Coordinador/a
Prologuista
Revisor/a
Ilustrador/a
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editor
MDPI
Resumen
Accurate 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.
Descripción
The 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
Categorías UNESCO
Palabras clave
time series forecasting, financial forecasting, recurrent neural network, BiLSTM, graph convolutional network
Citación
Lazcano, 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
Centro
Facultades y escuelas::E.T.S. de Ingeniería Informática
Departamento
Ingeniería de Software y Sistemas Informáticos