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A novel decomposition-ensemble approach to crude oil price forecasting with evolution clustering and combined model

  • Jiaming Zhu
  • Jinpei Liu
  • Peng Wu
  • Huayou ChenEmail author
  • Ligang Zhou
Original Article
  • 14 Downloads

Abstract

In order to deal with non-stationary and chaotic series, a hybrid forecasting approach is proposed in this study, which integrates ensemble empirical mode decomposition (EEMD) and optimal combined forecasting model (CFM). The proposed approach introduces a new intrinsic mode functions (IMFs) reconstruction method by using evolutionary clustering algorithm, and utilizes optimal combined model to forecast sub-series. Firstly, the EEMD technique is employed to sift the IMFs and a residue. Secondly, the comprehensive contribution index (CCI) of each IMF is calculated and IMFs are further reconstructed by evolutionary clustering algorithm according to CCI of each IMF. Then, a new sub-series called virtual intrinsic mode functions (VIMFs) is defined and obtained. Thirdly, the optimal combined forecasting model is developed to forecast the VIMFs and residues. In the end, the final forecasting results are obtained by summing the forecasts of VIMFs and residue. For illustration and comparison, the West Texas Intermediate (WTI) crude oil price data are shown as a numerical example. The research results show that the proposed approach outperforms benchmark models in terms of some forecasting assessment measures. Therefore, the proposed hybrid approach can be utilized as an effective model for the forecasting of crude oil price.

Keywords

Hybrid forecasting approach Ensemble empirical mode decomposition Mode reconstruction Optimal combined model Crude oil price 

Abbreviations

ANN

Artificial neural networks

IMF

Intrinsic mode function

SVR

Support vector regression

CFM

Combined forecasting model

DM test

Diebold–Mariano test

SSE

Sum of square error

MAE

Mean absolute error

RMSE

Root mean square error

EEMD

Ensemble empirical mode decomposition

VIMF

Virtual intrinsic mode function

NARNN

Nonlinear autoregressive neural network

GRNN

General regression neural network

ECA

Artificial neural networks

CCI

Comprehensive contribution index

IOWA

Induced ordered weighted averaging

MAPE

Mean absolute percentage error

Notes

Acknowledgements

The work was supported by National Natural Science Foundation of China (Nos. 71871001, 61502003, 71771001, 71701001, 71501002), University Provincial Natural Science Research Project of Anhui Province (No. KJ2017A026).

Compliance with ethical standards

Conflict of Interest

The authors declared that they have no conflicts of interest to this work.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jiaming Zhu
    • 1
  • Jinpei Liu
    • 2
  • Peng Wu
    • 1
  • Huayou Chen
    • 1
    Email author
  • Ligang Zhou
    • 1
    • 3
  1. 1.School of Mathematical SciencesAnhui UniversityHefeiChina
  2. 2.School of BusinessAnhui UniversityHefeiChina
  3. 3.China Institute of Manufacturing DevelopmentNanjing University of Information Science and TechnologyNanjingChina

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