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A New Method for Crude Oil Price Forecasting Based on Support Vector Machines

  • Wen Xie
  • Lean Yu
  • Shanying Xu
  • Shouyang Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3994)

Abstract

This paper proposes a new method for crude oil price forecasting based on support vector machine (SVM). The procedure of developing a support vector machine model for time series forecasting involves data sampling, sample preprocessing, training & learning and out-of-sample forecasting. To evaluate the forecasting ability of SVM, we compare its performance with those of ARIMA and BPNN. The experiment results show that SVM outperforms the other two methods and is a fairly good candidate for the crude oil price prediction.

Keywords

Support Vector Machine Root Mean Square Error Support Vector Regression Support Vector Machine Model ARIMA Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Wen Xie
    • 1
  • Lean Yu
    • 1
  • Shanying Xu
    • 1
  • Shouyang Wang
    • 1
  1. 1.Institute of Systems ScienceAcademy of Mathematics and Systems Sciences, Chinese Academy of SciencesBeijingChina

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