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A bi‐level ensemble learning approach to complex time series forecasting: Taking exchange rates as an example
16 references·3 citing·48 second hop
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References

  • 2002Comparing Predictive Accuracy1,745 cited
  • 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm705 cited
  • 2017A deep learning ensemble approach for crude oil price forecasting383 cited
  • 2016Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation330 cited
  • 2004A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates259 cited
  • 2016Exchange Rates and Monetary Policy Uncertainty244 cited
  • 2020Retail sales forecasting with meta-learning180 cited
  • 2020A novel text-based framework for forecasting agricultural futures using massive online news headlines179 cited
  • 2018Exploring the sources of uncertainty: Why does bagging for time series forecasting work?162 cited
  • 2014Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis161 cited
  • 2012Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting152 cited
  • 2021Using SARIMA–CNN–LSTM approach to forecast daily tourism demand130 cited
  • 2005Global Production Planning Under Exchange-Rate Uncertainty110 cited
  • 2019Combining wavelet decomposition with machine learning to forecast gold returns103 cited

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  • 2020A new ensemble deep learning approach for exchange rates forecasting and trading102 cited
  • 2019Predicting monthly biofuel production using a hybrid ensemble forecasting methodology100 cited
  • Cited by

    • 2023A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, and statistical models57 cited
    • 2023A dynamic clustering ensemble learning approach for crude oil price forecasting40 cited
    • 2023Causal carbon price interval prediction using lower upper bound estimation combined with asymmetric multi-objective evolutionary algorithm and long short-term memory37 cited