An effective and robust decomposition-ensemble energy price forecasting paradigm with local linear prediction

Quande Qin, Kangqiang Xie, Huangda He, Li Li, Xianghua Chu, Yi Ming Wei, Teresa Wu

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Energy price time series exhibit nonlinear and nonstationary features, which make accurate forecasting energy prices challenging. In this paper, we propose a novel decomposition-ensemble forecasting paradigm based on ensemble empirical mode decomposition (EEMD) and local linear prediction (LLP). The EEMD is used to decompose energy price time series into components, including several intrinsic mode functions and one residual with a simplified structure. Motivated by the findings of the fully local characteristics of a time series decomposed by the EEMD, we adopt the LLP technique to forecast each component. The forecasting results of all the components are aggregated as a final forecast. For validation, three types of energy price time series, crude oil, electricity and natural gas prices, are studied. The experimental results indicate that the proposed model achieves an improvement in terms of both level forecasting and direction forecasting. The performance of the proposed model is also validated through comparison with several energy price forecasting approaches from the literature. In addition, the robustness and the effects of the parameter settings of LLP are investigated. We conclude the proposed model is easy to implement and efficient for energy price forecasting.

Original languageEnglish (US)
Pages (from-to)402-414
Number of pages13
JournalEnergy Economics
StatePublished - Sep 2019


  • Energy price
  • Ensemble empirical mode decomposition
  • Forecasting
  • Local linear prediction

ASJC Scopus subject areas

  • Economics and Econometrics
  • Energy(all)


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