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

Hua Luo, Xing Liu, Shuang Wang

Abstract


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.

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DOI: http://dx.doi.org/10.21914/anziamj.v58i0.10995



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