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A multi-scale model with feature recognition for the use of energy futures price forecasting
16 references·0 citing·53 second hop
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References

  • 1996Fractionally integrated generalized autoregressive conditional heteroskedasticity2,290 cited
  • 2019Machine learning in energy economics and finance: A review438 cited
  • 2017A deep learning ensemble approach for crude oil price forecasting383 cited
  • 2019Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network254 cited
  • 2014Oil price and stock returns of consumers and producers of crude oil232 cited
  • 2015Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction175 cited
  • 2020A novel hybrid model for forecasting crude oil price based on time series decomposition137 cited
  • 2020Multistep-ahead forecasting of coal prices using a hybrid deep learning model135 cited
  • 2020A new hybrid model for forecasting Brent crude oil price133 cited
  • 2021Impacts of haze pollution on China's tourism industry: A system of economic loss analysis110 cited
  • 2017Short-term forecasting of natural gas prices using machine learning and feature selection algorithms98 cited
  • 2017Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression92 cited
  • 2022Does economic policy uncertainty drive nonlinear risk spillover in the commodity futures market?90 cited
  • 2020Energy futures and spots prices forecasting by hybrid SW-GRU with EMD and error evaluation

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77 cited
  • 2019Can CBOE gold and silver implied volatility help to forecast gold futures volatility in China? Evidence based on HAR and Ridge regression models69 cited
  • 2021A novel multiscale forecasting model for crude oil price time series69 cited
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