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Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm

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  • Jiang, Ping
  • Liu, Zhenkun
  • Wang, Jianzhou
  • Zhang, Lifang

Abstract

Effective crude oil and natural gas futures price forecasting is a crucial endeavor for financial energy markets and is also a challenging work due to the nonlinear and fluctuant characteristic of futures price time series. Most existing researches have failed at the consideration of both linear and nonlinear information, optimal sub-model selection, and interval forecasting. To bridge these gaps, a novel decomposition-selection-ensemble forecasting system is proposed to perform deterministic prediction and interval forecasting in this study, which is constituted by data decomposition method, optimal sub-model selection strategy, proposed multi-objective version of chaos game algorithm, and multiple forecasting models. The proposed forecasting system prominently prompted the forecasting accuracy and stability of energy futures price, and improved the applicability at dealing with different data characteristic. Empirical results based on energy futures price demonstrated that the point forecasting and interval forecasting results obtained from the proposed forecasting system are more reliable and stable relative to other comparative models; thus, it can provide useful references for national economic policies and operators in financial energy markets.

Suggested Citation

  • Jiang, Ping & Liu, Zhenkun & Wang, Jianzhou & Zhang, Lifang, 2021. "Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm," Resources Policy, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:jrpoli:v:73:y:2021:i:c:s0301420721002452
    DOI: 10.1016/j.resourpol.2021.102234
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    8. Zhang, Lifang & Wang, Jianzhou & Niu, Xinsong & Liu, Zhenkun, 2021. "Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection," Applied Energy, Elsevier, vol. 301(C).
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