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

Author

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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
    Faculty of Law, Business and Government, Universidad Francisco de Vitoria, 28223 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)

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.

Suggested Citation

  • Ana Lazcano & Pedro Javier Herrera & Manuel Monge, 2023. "A Combined Model Based on Recurrent Neural Networks and Graph Convolutional Networks for Financial Time Series Forecasting," Mathematics, MDPI, vol. 11(1), pages 1-21, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:1:p:224-:d:1022761
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    References listed on IDEAS

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    1. Dehghani, Hesam & Bogdanovic, Dejan, 2018. "Copper price estimation using bat algorithm," Resources Policy, Elsevier, vol. 55(C), pages 55-61.
    2. Mohammad Almasarweh & S. AL Wadi, 2018. "ARIMA Model in Predicting Banking Stock Market Data," Modern Applied Science, Canadian Center of Science and Education, vol. 12(11), pages 309-309, November.
    3. Tim Hill & Marcus O'Connor & William Remus, 1996. "Neural Network Models for Time Series Forecasts," Management Science, INFORMS, vol. 42(7), pages 1082-1092, July.
    4. Bernard Rosner & Robert J. Glynn & Mei-Ling T. Lee, 2006. "The Wilcoxon Signed Rank Test for Paired Comparisons of Clustered Data," Biometrics, The International Biometric Society, vol. 62(1), pages 185-192, March.
    5. A. Malliaris & Mary Malliaris, 2013. "Are oil, gold and the euro inter-related? Time series and neural network analysis," Review of Quantitative Finance and Accounting, Springer, vol. 40(1), pages 1-14, January.
    6. Manuel Monge & Ana Lazcano, 2022. "Commodity Prices after COVID-19: Persistence and Time Trends," Risks, MDPI, vol. 10(6), pages 1-20, June.
    7. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
    8. Bhardwaj, Geetesh & Swanson, Norman R., 2006. "An empirical investigation of the usefulness of ARFIMA models for predicting macroeconomic and financial time series," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 539-578.
    9. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    10. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
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    2. Qiong Yao & Chen Chen & Dan Song & Xiang Xu & Wensheng Li, 2023. "A Novel Finger Vein Verification Framework Based on Siamese Network and Gabor Residual Block," Mathematics, MDPI, vol. 11(14), pages 1-26, July.

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