A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/1/224 |
| _version_ | 1797431443018743808 |
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| author | Ana Lazcano Pedro Javier Herrera Manuel Monge |
| author_facet | Ana Lazcano Pedro Javier Herrera Manuel Monge |
| author_sort | Ana Lazcano |
| collection | DOAJ |
| description | 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 (R<sup>2</sup>). 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. |
| first_indexed | 2024-03-09T09:45:04Z |
| format | Article |
| id | doaj.art-bbf41689acc44da7b669b315c612a768 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T09:45:04Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-bbf41689acc44da7b669b315c612a7682023-12-02T00:39:12ZengMDPI AGMathematics2227-73902023-01-0111122410.3390/math11010224A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series ForecastingAna Lazcano0Pedro Javier Herrera1Manuel Monge2Department of Computer Systems and Software Engineering, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal, 16, 28040 Madrid, SpainDepartment of Computer Systems and Software Engineering, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal, 16, 28040 Madrid, SpainFaculty of Law, Business and Government, Universidad Francisco de Vitoria, 28223 Madrid, SpainAccurate 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 (R<sup>2</sup>). 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.https://www.mdpi.com/2227-7390/11/1/224time series forecastingfinancial forecastingrecurrent neural networkBiLSTMgraph convolutional network |
| spellingShingle | Ana Lazcano Pedro Javier Herrera Manuel Monge A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting Mathematics time series forecasting financial forecasting recurrent neural network BiLSTM graph convolutional network |
| title | A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting |
| title_full | A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting |
| title_fullStr | A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting |
| title_full_unstemmed | A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting |
| title_short | A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting |
| title_sort | combined model based on recurrent neural networks and graph convolutional networks for financial time series forecasting |
| topic | time series forecasting financial forecasting recurrent neural network BiLSTM graph convolutional network |
| url | https://www.mdpi.com/2227-7390/11/1/224 |
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