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A novel hybrid model for forecasting crude oil price based on time series decomposition
16 references·16 citing·43 second hopsampled
A novel hybrid model for forecasting crude oil price based on time series decomposition (2020)A novel hybrid model for forecasting crud…Statistical and Machine Learning forecasting methods: Concerns and ways forward (2018)Statistical and Machine Learn…Improved EEMD-based crude oil price forecasting using LSTM networks (2018)Improved EEMD-based crude oil…Forecasting risk with Markov-switching GARCH models:A large-scale performance study (2018)Forecasting risk with Markov-…A non-iterative decomposition-ensemble learning paradigm using RVFL network for crude oil price forecasting (2017)A non-iterative decomposition…A deep learning ensemble approach for crude oil price forecasting (2017)A deep learning ensemble appr…Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction (2015)Evolutionary Neural Network m…A novel hybrid method for crude oil price forecasting (2015)A novel hybrid method for 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…Forecasting Nonlinear Crude Oil Futures Prices (2006)Forecasting Nonlinear Crude O…Chapter 4 Forecast Combinations (2006)Chapter 4 Forecast Combinatio…Risk and Volatility: Econometric Models and Financial Practice (2004)Risk and Volatility: Economet…Assessing the Forecasting Performance of Regime‐Switching, ARIMA and GARCH Models of House Prices (2003)Assessing the Forecasting Per…Modeling the conditional distribution of interest rates as a regime-switching process (1996)Modeling the conditional dist…A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle (1989)A New Approach to the Economi…Generalized autoregressive conditional heteroskedasticity (1986)Generalized autoregressive co…Hybrid framework combining grey system model with Gaussian process and STL for CO 2 emissions forecasting in developed countries (2024)Hybrid framework combining gr…A novel crude oil price forecasting model using decomposition and deep learning networks (2024)A novel crude oil price forec…Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models (2023)Novel deterministic and proba…Crude oil price forecasting with machine learning and Google search data: An accuracy comparison of single-model versus multiple-model (2023)Crude oil price forecasting w…Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm (2022)Deterministic and uncertainty…Deep learning framework for predictive modeling of crude oil price for sustainable management in oil markets (2022)Deep learning framework for p…A novel crude oil prices forecasting model based on secondary decomposition (2022)A novel crude oil prices fore…A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting (2022)A new secondary decomposition…Forecasting crude oil prices based on variational mode decomposition and random sparse Bayesian learning (2021)Forecasting crude oil prices …Forecasting China’s sovereign CDS with a decomposition reconstruction strategy (2021)Forecasting China’s sovereign…Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm (2021)Decomposition-selection-ensem…Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns (2021)Data-driven based machine lea…A novel method for online real-time forecasting of crude oil price (2021)A novel method for online rea…A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features (2021)A novel crude oil price trend…Energy price prediction using data-driven models: A decade review (2020)Energy price prediction using…Effective crude oil price forecasting using new text-based and big-data-driven model (2020)Effective crude oil price for…The Combination of Forecasts (1969)Combining forecasts: A review and annotated bibliography (1989)The M3-Competition: results, conclusions and implications (2000)Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2009)Investor Sentiment Aligned: A Powerful Predictor of Stock Returns (2014)Forecasting: theory and practice (2022)Modeling and forecasting petroleum futures volatility (2006)A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting (2018)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)Time Series Analysis: Forecasting and Control (1977)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks (1993)Multivariate Simultaneous Generalized ARCH (1995)Conditional Heteroskedasticity in Asset Returns: A New Approach (1991)Forecasting oil price realized volatility using information channels from other asset classes (2017)Geopolitical risk and oil volatility: A new insight (2019)Trends and random walks in macroeconmic time series (1982)Co-Integration and Error Correction: Representation, Estimation, and Testing (1987)The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis (1989)ARCH modeling in finance (1992)Introductory Econometrics for Finance (2008)CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* (1964)A Theory of Volatility Spreads (2006)Chapter 15 Volatility and Correlation Forecasting (2006)Parametric and Nonparametric Volatility Measurement (2010)Testing for a Unit Root in Time Series Regression (1988)Comparing Predictive Accuracy (1994)Comparing Predictive Accuracy (1995)Stock price prediction based on deep neural networks (2019)A comprehensive evaluation of ensemble learning for stock-market prediction (2020)Another look at measures of forecast accuracy (2006)The M4 Competition: 100,000 time series and 61 forecasting methods (2019)Recurrent Neural Networks for Time Series Forecasting: Current status and future directions (2020)Asymmetric correlations of equity portfolios (2002)International Asset Allocation With Regime Shifts (2002)Forecasting Volatility in Financial Markets: A Review (2003)
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References

  • 1986Generalized autoregressive conditional heteroskedasticity22,329 cited
  • 1989A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle9,681 cited
  • 2018Statistical and Machine Learning forecasting methods: Concerns and ways forward1,515 cited
  • 1996Modeling the conditional distribution of interest rates as a regime-switching process1,492 cited
  • 2006Chapter 4 Forecast Combinations1,318 cited
  • 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm705 cited
  • 2004Risk and Volatility: Econometric Models and Financial Practice554 cited
  • 2017A deep learning ensemble approach for crude oil price forecasting383 cited
  • 2010Forecasting crude oil market volatility: Further evidence using GARCH-class models374 cited
  • 2015A novel hybrid method for crude oil price forecasting272 cited
  • 2003Assessing the Forecasting Performance of Regime‐Switching, ARIMA and GARCH Models of House Prices227 cited
  • 2018Improved EEMD-based crude oil price forecasting using LSTM networks220 cited
  • 2006Forecasting Nonlinear Crude Oil Futures Prices176 cited
  • 2015Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction175 cited

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2018
Forecasting risk with Markov-switching GARCH models:A large-scale performance study
167 cited
  • 2017A non-iterative decomposition-ensemble learning paradigm using RVFL network for crude oil price forecasting153 cited
  • Cited by

    • 2021Forecasting crude oil prices based on variational mode decomposition and random sparse Bayesian learning104 cited
    • 2020Effective crude oil price forecasting using new text-based and big-data-driven model101 cited
    • 2020Energy price prediction using data-driven models: A decade review91 cited
    • 2021Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm80 cited
    • 2021Data-driven based machine learning models for predicting the deliverability of underground natural gas storage in salt caverns67 cited
    • 2021A novel method for online real-time forecasting of crude oil price60 cited
    • 2022Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm57 cited
    • 2024Hybrid framework combining grey system model with Gaussian process and STL for CO 2 emissions forecasting in developed countries57 cited
    • 2022Deep learning framework for predictive modeling of crude oil price for sustainable management in oil markets56 cited
    • 2022A new secondary decomposition-reconstruction-ensemble approach for crude oil price forecasting54 cited
    • 2024A novel crude oil price forecasting model using decomposition and deep learning networks49 cited
    • 2021A novel crude oil price trend prediction method: Machine learning classification algorithm based on multi-modal data features48 cited
    • 2021Forecasting China’s sovereign CDS with a decomposition reconstruction strategy47 cited
    • 2023Crude oil price forecasting with machine learning and Google search data: An accuracy comparison of single-model versus multiple-model45 cited
    • 2023Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models44 cited
    • 2022A novel crude oil prices forecasting model based on secondary decomposition41 cited