CPPCNDL: Crude oil price prediction using complex network and deep learning algorithms

Abstract

Crude oil price prediction is a challenging task in oil producing countries. Its price is among the most complex and tough to model because fluctuations of price of crude oil are highly irregular, nonlinear and varies dynamically with high uncertainty. This paper proposed a hybrid model for crude oil price prediction that uses the complex network analysis and long short-term memory (LSTM) of the deep learning algorithms. The complex network analysis tool called the visibility graph is used to map the dataset on a network and K-core centrality was employed to extract the non-linearity features of crude oil and reconstruct the dataset. The complex network analysis is carried out in order to preprocess the original data to extract the non-linearity features and to reconstruct the data. Thereafter, LSTM was employed to model the reconstructed data. To verify the result, we compared the empirical results with other research in the literature. The experiments show that the proposed model has higher accuracy, and is more robust and reliable.

Keywords

Complex network analysis / Deep learning / Long-short term memory network / K-core centrality / Artificial intelligence / Crude oil price prediction

References

[1] Alley A. Asekomeh, H. Mobolaji Y.A. Adeniran, Oil Price shocks and the Nigerian economic growth, Eur. Sci. J. 10 (2014) 375-391.

[2] J. Zhang, Y. Zhang, L. Zhang, A novel hybrid method for crude oil price prediction, J. Energy Econ. (2015) 1-11, https://doi.org/10.1016/j.eneco.2015.02.018.

[3] C.O. Augustine, Exchange rate fluctuations, oil prices and economic performance: empirical evidence from Nigeria, Int. J. Energy Econ. Policy 5 (2015) 502-506.

[4] A. Shabri, R. Samsudin, Daily crude oil forecasting using hybridizing wavelength and artificial neural network model, Math. Probl. Eng. (2014) 1-10, https://doi. org/10.1155/2014/201402.

[5] Y. Zhao, J. Li, L. Yu, A deep learning assemble approach for crude oil price forecasting, J. Energy Econ. (2017) 1-18, https://doi.org/10.1016/j.eneco.2017.05.023.

[6] H. Chiroma, S. Abdulkareem, T. Herawan, Evolutionary neural network model for west Texas intermediate crude oil price prediction, Appl. Energy (2015) 266-273.

[7] M.M. Mostafa, A.A. El-masry, Oil price forecasting using gene expression programming and artificial neural networks, J. Econ. Model. 54 (2016) 40-53, https://doi.org/10.1016/j.econmod.2015.12.014.

[8] M.R. Mahdiani, E. Khamehchi, A modified neural network model for predicting the crude oil price, Journal of Intellectual Economics 10 (2016) 71-77.

[9] R. Tehrani, F. Khodayar, A hybrid optimized artificial intelligent model to forecast crude oil using genetic algorithm, Afr. J. Bus. Manag. 5 (2011) 13130-13135, https://doi.org/10.5897/AJBM11.304.

[10] H. Shin, T. Hou, K. Park, C. Park, S. Choi, Prediction of movement direction in crude oil price based on semi-supervised learning, Decis. Support Syst. 55 (2013) 348-358, https://doi.org/10.1016/j.dss.2012.11.009.

[11] L. Yu, W. Dai, L. Tang, A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting, J. Eng. Appl. Artif. Intell. (2016) 1-12, https://doi.org/10.1016/j.engappai.2015.04.016i.

[12] Y. Chen, K. He, K.G.F. Tso, Forecasting crude oil price: a deep learning-based model. International conference on information technology and quality management, Procedia Comput. Sci. 122 (2017) 300-307, https://doi.org/10.1016/j.procs.2017.11.373.

[13] H. Wang, F. Zhang, X. Xie, M. Guo, Deep knowledge-aware network for news Recommendation, ACM 2 (2018) 1-10.

[14] X. Ma, Y. Jin, Q. Dong, A generalized dynamic fuzzy neural network based on singular spectrum analysis optimized by brain storm optimization for short-term wind speed forecasting, Appl. Soft Comput. 54 (2017) 296-312.

[15] J. Wang, X. Li, T. Hong, S. Wang, A semi-heterogeneous approach to combining crude oil price forecasts, J. Inf. Sci. (2018) 279-292.

[16] I. Goodfellow, Y. Bengio, A. Courville, Deep Learning, The MIT Press, Cambridge MA, 2016.

[17] A. Prieto, B. Prieto, E. Martinez-Ortigosa, et al., Neural networks: an overview of early research, current frameworks and new challenges, Neurocomputing 214 (2016) 242-268.

[18] J. Patterson, A. Gibson, Deep Learning a Practitioners Approach, O'Reilly Media,Inc, Newton, MA, 2017.

[19] V. Sze, Y. Chen, T. Yang, J.S. Emer, Efficient processing of deep neural networks, A tutorial and survey, CoRRabs/1703.09039, 2017. http://arxiv.org/abs/1703. 09039.

[20] W. Liu, Z.X.L. Wang, et al., A survey of deep neural network architectures and their applications, Neurocomputing 234 (2017) 11-26.

[21] Y. Ding, A novel decompose-assemble methodology with AIC-ANN approach for crude oil forecasting, J. Energy 154 (2018) 328-336.

[22] H. Naser, Estimating and forecasting the real prices of crude oil: a data rich model using a dynamic model averaging (DMA) approach, Energy Econ. 56 (2016) 75-87.

[23] L. Fan, S. Pan, Z. Li, H. Li, An ica-based support vector regression scheme for forecasting crude oil prices, Technol. Forecast. Soc. Chang. 112 (2016) 245-253.

[24] O. Saltik, S. Degirmen, M. Ural, Volatility modelling in crude oil and natural gas price, Istanbul Conference of Economics and Finance, ICEF 2015, 22-23 October 2015, Istanbul, Turkey, 38 2015, pp. 476-491.

[25] F. Chiu, C. Hsu, A. Ho, C. Chen, Modeling the price relationships between crude oil, energy crops and biofuels, Energy 109 (2016) 845-857.

[26] A. Jedidzadeh, A. Serletis, The global crude oil market and biofuel agricultural commodity prices, J. Econ. Asymmetries 18 (2018) 1-9.

[27] A. Khashman, N. Nwulu, Intelligent Prediction of Crude Oil Price Using Support Vector Machines, Applied Machine Intelligence and Informatics (SAMI), 2011, IEEE 9th international symposium on IEEE, 2011, pp. 165-169.

[28] S. Li, Y. Ge, Crude oil price prediction based on dynamic correcting support vector regression machine, Abstr. Appl. Anal. (2013) 666-686.

[29] X. Li, I. Yu, I. Tang, W. Dai, Coupling Firefly Algorithm and Least Squares Support Vector Regression for Crude Oil Price Forecasting, Business Intelligence and Financial Engineering (BIFE), 2013 Sixth International Conference on IEEE, (2013), pp. 80-83.

[30] S. Gao, Y. Lei, A new approach for crude oil price prediction based on stream learning, J. Geosci. Front. 8 (2017) 183-187, https://doi.org/10.1016/j.gsf.2016.08.002.

[31] A.A. Godarzi, R.M. Amiri, A. Talaei, T. Jamasb, Predicting oil price movements: a dynamic neural network Approach, Energy Policy 68 (2014) 371-382.

[32] L. Yu, Y. Zhao, L. Tang, A compressed based AI learning paradigm for crude oil price forecasting, J. Energy Econ. 46 (2014) 236-245.

[33] L.A. Gabralla, R. Jammazi, A. Abraham, Oil Price Prediction Using Ensemble Machine Learning, 2013 International Conference on Computing, Electrical And Electronic Engineering (ICCEEE), 2013, pp. 674-679.

[34] R. Jammazi, C. Alou, Crude oil price forecasting: experimental evidence from wavelet decomposition and neural network modeling, Energy Econ. 34 (2012) 828-841.

[35] T. Xion, Y. Bao, Z. Hu, Beyond one-step-ahead forecasting: evaluation of alternative multi-step-ahead forecasting models for crude oil prices, Energy Econ. 40 (2013) 405-415.

[36] Q. Ji, H. Zhan, Y. Fan, Identification of global oil trade patterns: an empirical research based on complex network theory, J. Energy Conv. Manag. (2014) 1-10, https://doi.org/10.1016/j.enconman.2013.12.072.

[37] Y. Zou, M. Small, Z. Liu, J. Kurths, Complex network approach to characterized the statistical features of the sunspot series, New J. Phys. 46 (2014) 236-245, https://doi.org/10.1016/j.eneco.2014.09.019.

[38] L. Lacasa, B. Luque, F. Ballesteros, J. Luque, J.C. Nuño, From time series to complex networks: the visibility graph, Proc. Natl. Acad. Sci. U. S. A 105 (2008) 4972-497.

[39] N. Wang, D. Li, Q. Wang, Visibility graph analysis on quarterly macroeconomic series of China based on complex network theory, Physica A 391 (2012) 6543-6555.

[40] A.M. Nuñez, L. Lacasa, J.P. Gomez, B. Luque, Visibility algorithms: a short review,in: Y. Zhang (Ed.), New Frontiers in Graph Theory, InTech, Rijeka, Croatia, 2012, pp. 119-152.

[41] M. Kitsak, L.K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H.E. Stanley, et al., Identification of influential spreaders in complex networks, Nat. Phys. 6 (2010) 888-893.