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

Ana Lazcano (), Pedro Javier Herrera and Manuel Monge
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Ana Lazcano: Department of Computer Systems and Software Engineering, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal, 16, 28040 Madrid, Spain
Pedro Javier Herrera: Department of Computer Systems and Software Engineering, Universidad Nacional de Educación a Distancia (UNED), Juan del Rosal, 16, 28040 Madrid, Spain
Manuel Monge: Faculty of Law, Business and Government, Universidad Francisco de Vitoria, 28223 Madrid, Spain

Mathematics, 2023, vol. 11, issue 1, 1-21

Abstract: 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 2 ). 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.

Keywords: time series forecasting; financial forecasting; recurrent neural network; BiLSTM; graph convolutional network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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