Based on SARIMA-BP hybrid model and SSVM model of international crude oil price prediction research

We propose two hybrid prediction models for the international crude oil price: SARIMA-BP hybrid model; and SSVM model. The SARIMA-BP hybrid model combines seasonality analysis and autoregressive integrated moving average with back propagation neural network model. The SSVM model combines seasonality analysis with support vector machines. New York Mercantile Exchange (NYMEX) crude oil's monthly closing price, which ranges from January 2002 to April 2016, is selected as the experimental data sets. Experimental results are compared among the SARIMA-BP hybrid model, SSVM model and single SARIMA model. Empirical analysis shows that the SSVM model has highest prediction accuracy, and the single SARIMA model has lowest prediction accuracy. Thus, the SSVM model displays a better performance in oil price prediction. Further, the SSVM model predicts NYMEX crude oil's closing price will approach 50 dollars per barrel in May 2016. References Z. B. Zhou, X. C. Dong, Analysis about the seasonality of china's crude oil import based on x-12-arima, Energy 42 (42) (2012) 281–288. P. F. Pai, C. S. Lin, A hybrid arima and support vector machines model in stock price forecasting, Omega 33 (6) (2005) 497–505. T. Kriechbaumer, A. Angus, D. Parsons, M. R. Casado, An improved waveletcarima approach for forecasting metal prices, Resources Policy 39 (1) (2014) 32–41. T. Koutroumanidis, K. Ioannou, G. Arabatzis, Predicting fuelwood prices in greece with the use of arima models, artificial neural networks and a hybrid arimacann model, Energy Policy 37 (9) (2009) 3627–3634. H. Mohammadi, L. Su, International evidence on crude oil price dynamics: Applications of arima-garch models, Energy Economics 32 (5) (2010) 1001–1008. H. Chiroma, S. Abdulkareem, T. Herawan, Evolutionary neural network model for west texas intermediate crude oil price prediction, Applied Energy 142 (2015) 266–273. L. A. Laboissiere, R. A. S. Fernandes, G. G. Lage, Maximum and minimum stock price forecasting of brazilian power distribution companies based on artificial neural networks, Applied Soft Computing 35 (2015) 66–74. H. Al-Askar, A. J. Hussain, D. Al-Jumeily, N. Radi, Regularized dynamic self organized neural network inspired by the immune algorithm for financial time series prediction, Lecture Notes in Computer Science 8590 (2014) 56–62. V. Sampathkumar, M. H. Santhi, J. Vanjinathan, Forecasting the land price using statistical and neural network software, Procedia Computer Science 57 (2015) 112–121. J. P. Maran, B. Priya, Modeling of ultrasound assisted intensification of biodiesel production from neem ( azadirachta indica ) oil using response surface methodology and artificial neural network, Fuel 143 (2015) 262–267. D. Singhal, K. S. Swarup, Electricity price forecasting using artificial neural networks, International Journal of Electrical Power and Energy Systems 33 (44) (2008) 111–118. X. Y. Zeng, L. Shu, G. M. Huang, J. Jiang, Triangular fuzzy series forecasting based on grey model and neural network, Applied Mathematical Modelling 40 (3) (2015) 1717–1727. L. Fan, S. Pan, Z. Li, H. Li, An ica-based support vector regression scheme for forecasting crude oil prices, Technological Forecasting and Social Change. F. Wen, J. Xiao, H. E. Zhifang, X. Gong, Stock price prediction based on ssa and svm , Procedia Computer Science 31 (2014) 625–631. X. Guo, D. C. Li, A. Zhang, Improved support vector machine oil price forecast model based on genetic algorithm optimization parameters, Aasri Procedia 1 (4) (2012) 525–530. J. L. Zhang, Y. J. Zhang, L. Zhang, A novel hybrid method for crude oil price forecasting, Energy Economics 49 (2015) 649–659. C. C. Chang, C. J. Lin, Libsvm: A library for support vector machines, Acm Transactions on Intelligent Systems \(and\) Technology 2 (3, article 27) (2007) 389–396. S. E. Yang, L. Huang, Financial crisis warning model based on bp neural network, Systems Engineering-theory and Practice 25 (1) (2005) 12–19