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An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting
11 references·16 citing·38 second hop
An Adaptive Multiscale Ensemble Learning Paradigm for Nonstationary and Nonlinear Energy Price Time Series Forecasting (2016)An Adaptive Multiscale Ensemble Learning …A novel hybrid method for crude oil price forecasting (2015)A novel hybrid method for cru…A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting (2015)A novel decomposition ensembl…Forecasting carbon futures volatility using GARCH models with energy volatilities (2013)Forecasting carbon futures vo…Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities (2012)Modelling and forecasting fos…Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology (2012)Carbon price forecasting with…A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network (2012)A Novel Multiscale Ensemble C…A nonparametric GARCH model of crude oil price return volatility (2011)A nonparametric GARCH model o…A new approach for crude oil price analysis based on Empirical Mode Decomposition (2007)A new approach for crude oil …Comparing Predictive Accuracy (1994)Comparing Predictive AccuracyA Simple Nonparametric Test of Predictive Performance (1992)A Simple Nonparametric Test o…Time Series Analysis: Forecasting and Control (1977)Time Series Analysis: Forecas…Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode (2023)Improving the forecasting acc…Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm (2023)Carbon price prediction based…A hybrid forecasting approach for China's national carbon emission allowance prices with balanced accuracy and interpretability (2023)A hybrid forecasting approach…Hybrid intelligent 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robust decomposition-ensemble energy price forecasting paradigm with local linear prediction (2019)An effective and robust decom…An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting (2019)An Adaptive Hybrid Learning P…Measuring the risk of European carbon market: an empirical mode decomposition-based value at risk approach (2018)Measuring the risk of Europea…Achieving the carbon intensity target of China: A least squares support vector machine with mixture kernel function approach (2018)Achieving the carbon intensit…A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction (2018)A Novel Decomposition-Ensembl…Hilbert Spectra and Empirical Mode Decomposition: A Multiscale Event Analysis Method to Detect the Impact of Economic Crises on the European Carbon Market (2017)Hilbert Spectra and Empirical…Generalized autoregressive conditional heteroskedasticity (1986)Modeling and forecasting 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References

  • 1977Time Series Analysis: Forecasting and Control19,200 cited
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  • 2012Carbon price forecasting with a novel hybrid ARIMA and least squares support vector machines methodology376 cited
  • 2013Forecasting carbon futures volatility using GARCH models with energy volatilities346 cited
  • 2015A novel hybrid method for crude oil price forecasting272 cited
  • 2015A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting204 cited
  • 2011A nonparametric GARCH model of crude oil price return volatility183 cited
  • 2012Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities170 cited
  • 2012A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network114 cited

Cited by

  • 2022Carbon prices forecasting in quantiles109 cited
  • 2017Hilbert Spectra and Empirical Mode Decomposition: A Multiscale Event Analysis Method to Detect the Impact of Economic Crises on the European Carbon Market107 cited
  • 2018Achieving the carbon intensity target of China: A least squares support vector machine with mixture kernel function approach

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98 cited
  • 2021Deep long short-term memory based model for agricultural price forecasting88 cited
  • 2022Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine58 cited
  • 2022Early warning system to predict energy prices: the role of artificial intelligence and machine learning57 cited
  • 2018Measuring the risk of European carbon market: an empirical mode decomposition-based value at risk approach55 cited
  • 2023Improving the forecasting accuracy of interval-valued carbon price from a novel multi-scale framework with outliers detection: An improved interval-valued time series analysis mode54 cited
  • 2019An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting53 cited
  • 2018A Novel Decomposition-Ensemble Based Carbon Price Forecasting Model Integrated with Local Polynomial Prediction52 cited
  • 2023A hybrid forecasting approach for China's national carbon emission allowance prices with balanced accuracy and interpretability52 cited
  • 2019Analysis and Forecasting of the Carbon Price in China’s Regional Carbon Markets Based on Fast Ensemble Empirical Mode Decomposition, Phase Space Reconstruction, and an Improved Extreme Learning Machine50 cited
  • 2023Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm38 cited
  • 2020A hybrid model for financial time‐series forecasting based on mixed methodologies36 cited
  • 2019An effective and robust decomposition-ensemble energy price forecasting paradigm with local linear prediction35 cited
  • 2020Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market35 cited