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Predicting monthly biofuel production using a hybrid ensemble forecasting methodology
8 references·13 citing·55 second hop
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this work references cited by second hop

References

  • 1994Time series prediction: Forecasting the future and understanding the past841 cited
  • 2007A new approach for crude oil price analysis based on Empirical Mode Decomposition447 cited
  • 2018Green loan and subsidy for promoting clean production innovation414 cited
  • 2015A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting204 cited
  • 2016Financial time series prediction using a dendritic neuron model183 cited
  • 2012The impacts of biofuels targets on land‐use change and food supply: A global CGE assessment166 cited
  • 2006A new boosting algorithm for improved time-series forecasting with recurrent neural networks140 cited
  • 2014A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting138 cited

Cited by

  • 2022Forecasting the crude oil prices with an EMD-ISBM-FNN model73 cited
  • 2021Multiscale financial risk contagion between international stock markets: Evidence from EMD-Copula-CoVaR analysis69 cited
  • 2020Optimal selection of heterogeneous ensemble strategies of time series forecasting with multi-objective programming67 cited
  • 2022Multivariate cryptocurrency prediction: comparative analysis of three recurrent neural networks approaches57 cited
  • 2023A bi‐level ensemble learning approach to complex time series forecasting: Taking exchange rates as an example57 cited

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  • 2022A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization51 cited
  • 2021Forecasting China’s sovereign CDS with a decomposition reconstruction strategy47 cited
  • 2021A novel grey seasonal model based on cycle accumulation generation for forecasting energy consumption in China40 cited
  • 2020An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China36 cited
  • 2021A memory-trait-driven decomposition–reconstruction–ensemble learning paradigm for oil price forecasting35 cited
  • 2023A new hybrid deep learning model for monthly oil prices forecasting34 cited
  • 2025A genetic algorithm optimized hybrid model for agricultural price forecasting based on VMD and LSTM network30 cited
  • 202330 years of exchange rate analysis and forecasting: A bibliometric review26 cited