• Muhammad Aamir Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia
  • Ani Shabri Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia
  • Muhammad Ishaq Department of Mathematics, School of Natural Sciences, National University of Sciences and Technology, Islamabad, Pakistan



ARIMA, CEEMDAN, Crude Oil, EMD, Kalman Filter


This paper used complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) based hybrid model for the forecasting of world crude oil prices. For this purpose, the crude oil prices original time series are decomposed into sub small finite series called intrinsic mode functions (IMFs). Then ARIMA model was applied to each extracted IMF to estimate the parameters. Next, using these estimated parameters of each ARIMA model, the Kalman Filter was run for each IMF, so that these extracted IMFs can be predicted more accurately. Finally, all IMFs are combined to get the result. For testing and verification of the proposed method, two crude oil prices were used as a sample i.e. Brent and WTI (West Texas Intermediate) crude oil monthly prices series. The D-statistic values of the proposed model were 93.33% for Brent and 89.29% for WTI which reveals the importance of the CEEMDAN based hybrid model.

Author Biographies

  • Muhammad Aamir, Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia
    PhD Candidate, Mathematical Sciences Department
  • Ani Shabri, Department of Mathematical Sciences, Faculty of Science, Universiti Teknologi Malaysia
    Mathematical Sciences Department, Senior Lecturer
  • Muhammad Ishaq, Department of Mathematics, School of Natural Sciences, National University of Sciences and Technology, Islamabad, Pakistan
    Department of Mathematics, Assistant Professor


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