Abstract
This paper studied deterministic chaotic behaviour and prediction of WTI crude oil daily price time series from 2015 to 2020 in Ecuador. To understand the price of crude oil, the dynamics and time delay of the system were reconstructed through the Average Mutual Information and False Nearest Neighbours methods, the chaotic characteristics was determined using the Lyapunov exponent, and finally a BDS test was applied to determine the nonlinearity of the series. Then, three neural networks are used to predict oil prices, which were validated by estimating four goodness-of-fit measures. The results show that the neural network models produce a good prediction rate, confirmed by a maximum error of \(0.0058927\%\) from the Radial Basis Function, which indicates a significant similarity between the prediction and the real data. Predictions made with monthly values perform better than those made with daily values, possibly due to the level of noise in the daily time series. Among the three models, NARX performs best, with a percentage error of \(2.6213\cdot 10^{- 13}\%\).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). http://tensorflow.org/
Alvarez-Ramirez, J., Cisneros, M., Ibarra Valdez, C., Soriano, A.: Multifractal hurst analysis of crude oil prices. Phys. A Stat. Mech. Appl. 313, 651–670 (2002)
Ardalani-Farsa, M., Zolfaghari, S.: Chaotic time series prediction with residual analysis method using hybrid elman-narx neural networks. Neurocomputing 73, 2540–2553 (2010)
BCE: Sector Petrolero (2020). https://contenido.bce.fin.ec/documentos/Administracion/bi_menuPetroleos.html#
Boullé, N., Dallas, V., Nakatsukasa, Y., Samaddar, D.: Classification of chaotic time series with deep learning. Phys. D Nonlinear Phenom. 403, 132261 (2019)
Broock, W., Scheinkman, J., Dechert, W., Lebaron, B.: A test for independence based on the correlation dimension. Econometric Rev. 15, 197–235 (1996)
Bruijn, S., Meijer, O., Beek, P., Van Dieen, J.: Assessing the stability of human locomotion: a review of current measures. J. R. Soc. Interface 10, 20120999 (2013)
Chirivella, X., Ortega-Becea, J., Infante, S.: Análisis no lineal de la frecuencia cardíaca fetal. Revista de obstetricia y ginecología de Venezuela 71(3), 174–182 (2011)
Chiroma, H., Abdulkareem, S., Herawan, T.: Evolutionary neural network model for west texas intermediate crude oil price prediction. Appl. Energ. 142, 266–273 (2015)
Drachal, K.: Forecasting spot oil price in a dynamic model averaging framework: have the determinants changed over time? Energ. Econ. 60, 35–46 (2016)
Farmer, J., Sidorowich, J.: Exploiting chaos to predict the future and reduce noise. In: Evolution, Learning and Cognition, January 1988
Fraser, A., Swinney, H.: Independent coordinates for strange attractors from mutual information. Phys. Rev. A 33, 1134–1140 (1986)
Garcia, M., Ruiz, J., Sanz, B.: El test BDS: Posibles limitaciones. Rect@ Actas 9 (2001)
Grassberger, P., Procaccia, I.: Measuring strangeness strange attractors. Phys. D Nonlinear Phenom. 9, 189–208 (1983)
Greenwood, P., Nikulin, M.: A Guide to Chi-squared Testing, vol. 39. Wiley-Interscience, Hoboken (1996)
Gupta, N., Nigam, S.: Crude oil price prediction using artificial neural network. Procedia Comput. Sci. 170, 642–647 (2020)
Hagan, M., Demuth, H., Beale, M.: Neural Network Design, vol. 2 pp. 2–14. Pws Pub, Boston (1996)
He, L.Y., Chen, S.P.: Are crude oil markets multifractal? evidence from MF-DFA and MF-SSA perspectives. Phys. A Stat. Mech. Appl. Phys. A 389, 3218–3229 (2010)
Infante, S., Ortega, J., Cedeño, F.: Estimación de datos faltantes en estaciones meteorológicas de venezuela vía un modelo de redes neuronales. Rev. Climatol. 8, 51–70 (2008)
Kennel, M., Brown, R., Abarbanel, H.: Determining embedding dimension for phase-space reconstruction using a geometrical construction. Phys. Rev. A 45, 3403 (1992)
Kuchaki Rafsanjani, M., Samareh, M.: Chaotic time series prediction by artificial neural networks. J. Comput. Methods Sci. Eng. 16, 1–17 (2016)
Kugiumtzis, D., Bjørn, L., Christophersen, N.: Chaotic time series part i: estimation of invariant properies in state space. Model. Identificat. Control 15 (1994)
MATLAB: Neural net time series toolbox (2010). https://la.mathworks.com/help/deeplearning/ref/neuralnettimeseries-app.html
Menezes, J.M., Jr., Barreto, G.: Long-term time series prediction with the narx network: an empirical evaluation. Neurocomputing 71, 3335–3343 (2008)
Moody, J., Darken, C.: Fast learning in networks of locally-tuned processing units. Neural Comput. 1, 281–294 (1989)
Orojo, O., Tepper, J., Mcginnity, T., Mahmud, M.: A multi-recurrent network for crude oil price prediction (2020)
Piorek, M.: Analysis of Chaotic Behavior in Non-linear Dynamical Systems Models and Algorithms for Quaternions. Springer International Publishing, Cham (2019). https://doi.org/10.1007/978-3-319-94887-4
Schuster, H.: Deterministic Chaos: An Introduction. Wiley-VCH Verlag, Weinheim (1984)
Shiblee, M., Kalra, P., Chandra, B.: Time series prediction with multilayer perceptron (mlp): a new generalized error based approach. In: Advances in Neuro-Information Processing: 15th International Conference, pp. 37–44, November 2008
Singh, V., Kumar, P., Nishant, S.: Feedback spillover dynamics of crude oil and global assets indicators: a system-wide network perspective. Energ. Econ. 80, 321–335 (2019)
Smith, R.: Estimating dimensions in noisy chaotic time series. J. R. Stat. Soc. Ser. B (Methodol.) 54, 329–351 (1992)
Trapletti, A., Hornik, K.: Tseries: time series analysis and computational finance (2020). https://CRAN.R-project.org/package=tseries
Xiu, Y., Zhang, W.: Multivariate chaotic time series prediction based on narx neural networks. In: Proceedings of the 2nd International Conference on Electrical, Automation and Mechanical Engineering, pp. 164–167 (2017)
Yin, T., Wang, Y.: Predicting the price of WTI crude oil using ANN and chaos. Sustainability 11, 5980 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Cedeño, N., Carillo, G., Ayala, M.J., Lalvay, S., Infante, S. (2021). Analysis of Chaos and Predicting the Price of Crude Oil in Ecuador Using Deep Learning Models. In: Guarda, T., Portela, F., Santos, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2021. Communications in Computer and Information Science, vol 1485. Springer, Cham. https://doi.org/10.1007/978-3-030-90241-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-90241-4_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90240-7
Online ISBN: 978-3-030-90241-4
eBook Packages: Computer ScienceComputer Science (R0)