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An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting
16 references·5 citing·40 second hop
An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting (2019)An Adaptive Hybrid Learning Paradigm Inte…A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting (2018)A novel multiscale nonlinear …A novel hybrid method for crude oil price forecasting (2015)A novel hybrid method for cru…WHAT CENTRAL BANKERS NEED TO KNOW ABOUT FORECASTING OIL PRICES (2014)WHAT CENTRAL BANKERS NEED TO …A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting (2014)A Novel CEEMD-Based EELM Ense…Real-Time Forecasts of the Real Price of Oil (2012)Real-Time Forecasts of the Re…Crude oil price analysis and forecasting using wavelet decomposed ensemble model (2012)Crude oil price analysis and …Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling (2011)Crude oil price forecasting: …A nonparametric GARCH model of crude oil price return volatility (2011)A nonparametric GARCH model o…International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models (2010)International evidence on cru…Forecasting crude oil market volatility: Further evidence using GARCH-class models (2010)Forecasting crude oil market …Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008)Forecasting crude oil price w…A New Method for Crude Oil Price Forecasting Based on Support Vector Machines (2006)A New Method for Crude Oil Pr…Modeling and forecasting cointegrated relationships among heavy oil and product prices (2005)Modeling and forecasting coin…A semiparametric approach to short-term oil price forecasting (2001)A semiparametric approach to …Comparing Predictive Accuracy (1995)Comparing Predictive AccuracyComparing Predictive Accuracy (1994)Comparing Predictive AccuracyA novel crude oil prices forecasting model based on secondary decomposition (2022)A novel crude oil prices fore…Multi-Perspective Crude Oil Price Forecasting with a New Decomposition-Ensemble Framework (2021)Multi-Perspective Crude Oil P…Forecasting Crude Oil Price Using Event Extraction (2021)Forecasting Crude Oil Price U…Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market (2020)Daily Crude Oil Price Forecas…Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors (2019)Forecasting Daily Crude Oil P…Forecasting stock market movement direction with support vector machine (2004)Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method (2009)A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting (2015)Online big data-driven oil consumption forecasting with Google trends (2018)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Generalized autoregressive conditional heteroskedasticity (1986)Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)A COMPARISON OF VAR AND NEURAL NETWORKS WITH GENETIC ALGORITHM IN FORECASTING PRICE OF OIL (2004)Modelling and forecasting fossil fuels, CO2 and electricity prices and their volatilities (2012)Modelling oil price volatility with structural breaks (2012)Modeling and forecasting petroleum futures volatility (2006)Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization (2021)Price forecasting through neural networks for crude oil, heating oil, and natural gas (2024)Time Series Analysis Forecasting and Control. (1971)Time Series Analysis: Forecasting and Control (1977)Time series prediction: Forecasting the future and understanding the past (1994)Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold (2013)Machine learning in energy economics and finance: A review (2019)A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series (2020)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Forecasting oil price realized volatility using information channels from other asset classes (2017)Geopolitical risk and oil volatility: A new insight (2019)Designing a neural network for forecasting financial and economic time series (1996)Forecasting with artificial neural networks: (1998)Wavelet-based evidence of the impact of oil prices on stock returns (2013)Improved EEMD-based crude oil price forecasting using LSTM networks (2018)Oil and the Macroeconomy When Prices Go Up and Down: An Extension of Hamilton's Results (1989)Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market (2009)Quantifying the speculative component in the real price of oil: The role of global oil inventories (2013)The Role of Speculation in Oil Markets: What Have We Learned So Far? 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References

  • 1994Comparing Predictive Accuracy5,495 cited
  • 1995Comparing Predictive Accuracy5,214 cited
  • 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm705 cited
  • 2010Forecasting crude oil market volatility: Further evidence using GARCH-class models374 cited
  • 2012Real-Time Forecasts of the Real Price of Oil356 cited
  • 2010International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models346 cited
  • 2011Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling282 cited
  • 2015A novel hybrid method for crude oil price forecasting272 cited
  • 2006A New Method for Crude Oil Price Forecasting Based on Support Vector Machines258 cited
  • 2001A semiparametric approach to short-term oil price forecasting253 cited
  • 2018A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting235 cited
  • 2011A nonparametric GARCH model of crude oil price return volatility183 cited
  • 2005Modeling and forecasting cointegrated relationships among heavy oil and product prices172 cited
  • 2014WHAT CENTRAL BANKERS NEED TO KNOW ABOUT FORECASTING OIL PRICES167 cited
  • 2012Crude oil price analysis and forecasting using wavelet decomposed ensemble model

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146 cited
  • 2014A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting138 cited
  • Cited by

    • 2021Multi-Perspective Crude Oil Price Forecasting with a New Decomposition-Ensemble Framework46 cited
    • 2022A novel crude oil prices forecasting model based on secondary decomposition41 cited
    • 2021Forecasting Crude Oil Price Using Event Extraction41 cited
    • 2019Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors36 cited
    • 2020Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market35 cited