A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting

A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil... •A decomposition–ensemble model is proposed for crude oil price forecasting.•A data-characteristic-driven reconstruction is formulated and introduced.•Four steps are involved: decomposition, reconstruction, prediction and ensemble.•Empirical study statistically verifies the effectiveness of the proposed model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Energy Elsevier

A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting

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Publisher
Elsevier
Copyright
Copyright © 2015 Elsevier Ltd
ISSN
0306-2619
D.O.I.
10.1016/j.apenergy.2015.07.025
Publisher site
See Article on Publisher Site

Abstract

•A decomposition–ensemble model is proposed for crude oil price forecasting.•A data-characteristic-driven reconstruction is formulated and introduced.•Four steps are involved: decomposition, reconstruction, prediction and ensemble.•Empirical study statistically verifies the effectiveness of the proposed model.

Journal

Applied EnergyElsevier

Published: Oct 15, 2015

References

  • A novel data-characteristic-driven modeling methodology for nuclear energy consumption forecasting
    Tang, L.; Yu, L.; He, K.J.
  • Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction
    Chiroma, H.; Abdulkareem, S.; Herawan, T.
  • A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting
    Tang, L.; Yu, L.; Wang, S.; Li, J.P.; Wang, S.Y.
  • Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks
    Liu, H.; Tian, H.Q.; Pan, D.F.; Li, Y.F.
  • An advanced wind speed multi-step ahead forecasting approach with characteristic component analysis
    Zhang, G.; Wu, Y.; Liu, Y.
  • A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting
    Tang, L.; Wang, S.; He, K.J.; Wang, S.Y.
  • Forecasting short-run crude oil price using high-and low-inventory variables
    Ye, M.; Zyren, J.; Shore, J.
  • The study and application of a novel hybrid forecasting model–a case study of wind speed forecasting in China
    Wang, J.Z.; Wang, Y.; Jiang, P.

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