SZodixStudyAcademic Library
ShelvesOpen access
Study · an open academic library·Metadata from OpenAlex (CC0)·Shelves·OpenAlex metadata, de-duplicated
A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting
7 references·16 citing·43 second hop
A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting (2015)A decomposition–ensemble model with data-…Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling (2011)Crude oil price forecasting: …Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008)Forecasting crude oil price w…A new approach for crude oil price analysis based on Empirical Mode Decomposition (2007)A new approach for crude oil …A New Method for Crude Oil Price Forecasting Based on Support Vector Machines (2006)A New Method for Crude Oil Pr…Comparing Predictive Accuracy (1995)Comparing Predictive AccuracyUse of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance (1994)Use of Cumulative Sums of Squ…Comparing Predictive Accuracy (1994)Comparing Predictive AccuracyFinancing efficiency in natural resource markets mobilizing private and public capital for a green recovery (2023)Financing efficiency in natur…Forecasting crude oil prices: A scaled PCA approach (2021)Forecasting crude oil prices:…A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms (2021)A hybrid model for multi-step…Energy price prediction using data-driven models: A decade review (2020)Energy price prediction using…A new hybrid model for forecasting Brent crude oil price (2020)A new hybrid model for foreca…A decomposition-ensemble approach for tourism forecasting (2020)A decomposition-ensemble appr…Predicting monthly biofuel production using a hybrid ensemble forecasting methodology (2019)Predicting monthly biofuel pr…Monthly crude oil spot price forecasting using variational mode decomposition (2019)Monthly crude oil spot price …A hybrid VMD–BiGRU model for rubber futures time series forecasting (2019)A hybrid VMD–BiGRU model for …Oil price forecasting using a hybrid model (2018)Oil price forecasting using a…Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer (2018)Crude oil price prediction mo…Carbon price forecasting with variational mode decomposition and optimal combined model (2018)Carbon price forecasting with…A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting (2018)A novel decompose-ensemble me…A multiscale analysis for carbon price drivers (2018)A multiscale analysis for car…A hybrid model based on selective ensemble for energy consumption forecasting in China (2018)A hybrid model based on selec…How does investor attention affect international crude oil prices? (2017)How does investor attention a…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)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)Imperfect Competition and the Effects of Energy Price Increases on Economic Activity (1996)The Long-Run Evolution of Energy Prices (1999)Applications of Hilbert–Huang transform to non‐stationary financial time series analysis (2003)A novel hybrid method for crude oil price forecasting (2015)A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting (2018)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)A Guide to Simulation (1983)Volatility in Emerging Stock Markets (1999)Selection of estimation window in the presence of breaks (2006)Dynamic spillover effects among crude oil, precious metal, and agricultural commodity futures markets (2016)Time series forecasting using a hybrid ARIMA and neural network model (2003)A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer (2019)Long-term forecast of energy commodities price using machine learning (2019)Exploring the nexus between monetary uncertainty and volatility in global crude oil: A contemporary approach of regime-switching (2023)Statistical analysis of cointegration vectors (1988)Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian Vector Autoregressive Models (1991)Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series (1999)Nonlinear dependence and connectedness between clean/renewable energy sector equity and European emission allowance prices (2021)Asymmetric causality of economic policy uncertainty and oil volatility index on time-varying nexus of the clean energy, carbon and green bond (2022)Do green bonds and economic policy uncertainty matter for carbon price? New insights from a TVP-VAR framework (2023)The Combination of Forecasts (1969)Point and interval forecasting for carbon price based on an improved analysis-forecast system (2019)A combination forecasting model based on hybrid interval multi-scale decomposition: Application to interval-valued carbon price forecasting (2021)Forecasting carbon price using a multi‐objective least squares support vector machine with mixture kernels (2021)Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts (1998)A Comprehensive Look at The Empirical Performance of Equity Premium Prediction (2007)Strictly Proper Scoring Rules, Prediction, and Estimation (2007)Testing the equality of prediction mean squared errors (1997)Consumption, Aggregate Wealth, and Expected Stock Returns (2001)Deep learning with long short-term memory networks for financial market predictions (2017)
this work references cited by second hop

References

  • 1994Comparing Predictive Accuracy5,495 cited
  • 1995Comparing Predictive Accuracy5,214 cited
  • 1994Use of Cumulative Sums of Squares for Retrospective Detection of Changes of Variance1,180 cited
  • 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm705 cited
  • 2007A new approach for crude oil price analysis based on Empirical Mode Decomposition447 cited
  • 2011Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling282 cited
  • 2006A New Method for Crude Oil Price Forecasting Based on Support Vector Machines258 cited

Cited by

  • 2018A multiscale analysis for carbon price drivers203 cited
  • 2018Oil price forecasting using a hybrid model176 cited
  • 2018Carbon price forecasting with variational mode decomposition and optimal combined model142 cited
  • 2019A hybrid VMD–BiGRU model for rubber futures time series forecasting140 cited
  • 2018Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer139 cited
  • 2020A new hybrid model for forecasting Brent crude oil price133 cited
  • 2018A hybrid model based on selective ensemble for energy consumption forecasting in China126 cited

Only edges whose both ends are imported exist here. Widen the year window, or show the whole map. Back to the work

  • 2021A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms122 cited
  • 2021Forecasting crude oil prices: A scaled PCA approach116 cited
  • 2019Monthly crude oil spot price forecasting using variational mode decomposition107 cited
  • 2023Financing efficiency in natural resource markets mobilizing private and public capital for a green recovery104 cited
  • 2017How does investor attention affect international crude oil prices?102 cited
  • 2019Predicting monthly biofuel production using a hybrid ensemble forecasting methodology100 cited
  • 2020A decomposition-ensemble approach for tourism forecasting99 cited
  • 2018A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting95 cited
  • 2020Energy price prediction using data-driven models: A decade review91 cited