Research graph
References from Forecasting China’s Crude Oil Futures Price by Recurrent Neural Network Method Based on Unconstrained Transformation. Local targets link to admitted publications; unresolved targets remain external evidence.
A generalized pattern matching approach for multi-step prediction of crude oil price
10.1016/j.eneco.2006.10.012 · 2008 · External reference
Forecasting crude oil futures price using machine learning methods: Evidence from China
10.1016/j.eneco.2023.107089 · 2023 · External reference
Oil price shocks in a data-rich environment
10.1016/j.eneco.2014.07.006 · 2014 · External reference
Oil and the Macroeconomy since World War II
10.1086/261140 · 1983 · External reference
Extreme risk spillovers between SC, WTI and Brent crude oil futures-Evidence from time-varying Granger causality test
10.1016/j.energy.2025.135495 · 2025 · External reference
10.3389/fenvs.2021.636903
10.3389/fenvs.2021.636903 · External reference
How does digital village alleviate rural household energy poverty?
10.1016/j.energy.2025.134713 · 2025 · External reference
10.1371/journal.pone.0297554
10.1371/journal.pone.0297554 · External reference
A novel hybrid approach to forecast crude oil futures using intraday data
10.1016/j.techfore.2020.120126 · 2020 · External reference
China’s crude oil futures: Introduction and some stylized facts
10.1016/j.frl.2018.06.005 · 2019 · External reference
Evidence of the internationalization of China’s crude oil futures: Asymmetric linkages to global financial risks
10.1016/j.eneco.2023.107083 · 2023 · External reference
The pricing efficiency of crude oil futures in the Shanghai International Exchange
10.1016/j.frl.2019.101329 · 2020 · External reference
Price discovery in China’s crude oil futures markets: An emerging Asian benchmark?
10.1002/fut.22384 · 2022 · External reference
10.3389/fenrg.2022.741018
10.3389/fenrg.2022.741018 · External reference
Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting
10.1016/j.eneco.2021.105622 · 2021 · External reference
Global financial uncertainties and China’s crude oil futures market: Evidence from interday and intraday price dynamics
10.1016/j.eneco.2021.105149 · 2021 · External reference
The spillover and comovement of downside and upside tail risks among crude oil futures markets
10.1016/j.irfa.2024.103578 · 2024 · External reference
The impact of climate risks on global energy production and consumption: New evidence from causality-in-quantile and wavelet analysis
10.1016/j.energy.2025.134850 · 2025 · External reference
Good volatility, bad volatility and economic uncertainty: Evidence from the crude oil futures market
10.1016/j.energy.2021.119924 · 2021 · External reference
The connectedness between crude oil and financial markets: Evidence from implied volatility indices
10.1016/j.jcomm.2016.11.002 · 2016 · External reference
Do China’s macro-financial factors determine the Shanghai crude oil futures market?
10.1016/j.irfa.2021.101953 · 2021 · External reference
Examining the dynamic effect of COVID-19 pandemic on dwindling oil prices using structural vector autoregressive model
10.1016/j.energy.2021.120813 · 2021 · External reference
Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method
10.1016/j.eneco.2022.106049 · 2022 · External reference
Price discovery efficiency of China’s crude oil futures: Evidence from the Shanghai crude oil futures market
10.1016/j.eneco.2022.106172 · 2022 · External reference
10.3390/en15061955
10.3390/en15061955 · External reference
A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting
10.1016/j.apenergy.2015.07.025 · 2015 · External reference
Forecasting the crude oil prices with an EMD-ISBM-FNN model
10.1016/j.energy.2022.125407 · 2023 · External reference
Interval decomposition ensemble approach for crude oil price forecasting
10.1016/j.eneco.2018.10.015 · 2018 · External reference
Threshold autoregressive models for interval-valued time series data
10.1016/j.jeconom.2018.06.009 · 2018 · External reference
Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm
10.1007/s10479-022-04781-6 · 2025 · External reference
Forecasting interval-valued returns of crude oil: A novel kernel-based approach
10.1002/for.3167 · 2024 · External reference
Interval time series forecasting: A systematic literature review
10.1002/for.3024 · 2024 · External reference
Economic forecast evaluation: Profits versus the conventional error measures
1991 · External reference
Forecasting the WTI crude oil price by a hybrid-refined method
10.1016/j.eneco.2018.02.004 · 2018 · External reference
Forecasting nonlinear crude oil futures prices
10.5547/issn0195-6574-ej-vol27-no4-4 · 2006 · External reference
Modeling crude oil volatility using economic sentiment analysis and opinion mining of investors via deep learning and machine learning models
10.1016/j.energy.2023.130017 · 2024 · External reference
Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model
10.1016/j.eneco.2024.107588 · 2024 · External reference
The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China
10.1016/j.resourpol.2021.102173 · 2021 · External reference
Forecasting the prices of crude oil: An iterated combination approach
10.1016/j.eneco.2018.01.027 · 2018 · External reference
Forecasting the prices of crude oil using the predictor, economic and combined constraints
10.1016/j.econmod.2018.06.020 · 2018 · External reference
China’s Crude oil futures forecasting with search engine data
10.1016/j.procs.2022.11.266 · 2022 · External reference
Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism
10.1016/j.econmod.2021.01.005 · 2021 · External reference
Study on the impacts of Shanghai crude oil futures on global oil market and oil industry based on VECM and DAG models
10.1016/j.energy.2021.120050 · 2021 · External reference
Text-based crude oil price forecasting: A deep learning approach
10.1016/j.ijforecast.2018.07.006 · 2019 · External reference
Research on crude oil futures price prediction methods: A perspective based on quantum deep learning
10.1016/j.energy.2025.135080 · 2025 · External reference
Investor sentiment and machine learning: Predicting the price of China’s crude oil futures market
10.1016/j.energy.2022.123471 · 2022 · External reference
Crude oil future price forecasting using pretrained transformer model
10.1016/j.procs.2024.08.234 · 2024 · External reference
A novel hybrid forecasting system for crude oil futures prices: A dual perspective of deterministic forecasting and uncertainty analysis
10.1016/j.heliyon.2024.e39818 · 2024 · External reference
10.1371/journal.pone.0249852
10.1371/journal.pone.0249852 · External reference
A structural VAR and VECM modeling method for open-high-low-close data contained in candlestick chart
10.1186/s40854-024-00622-6 · 2024 · External reference
Macroeconomics and reality
10.2307/1912017 · 1980 · External reference
Statistical analysis of cointegration vectors
10.1016/0165-1889(88)90041-3 · 1988 · External reference
Cointegration and Error Correction Representation, Estimation and Testing
10.2307/1913236 · 1987 · External reference
Unresolved reference
External reference
A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
10.1007/bf00344251 · 1980 · External reference
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
10.1109/tvcg.2020.3030418 · 2021 · External reference
10.1109/ssci.2017.8285188
10.1109/ssci.2017.8285188 · External reference
CNNpred: CNN-based stock market prediction using a diverse set of variables
10.1016/j.eswa.2019.03.029 · 2019 · External reference
Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions
10.1016/j.frl.2024.105840 · 2024 · External reference
Long Short-Term Memory
10.1162/neco.1997.9.8.1735 · 1997 · External reference
Hybrid approach to the Japanese candlestick method for f inancial forecasting
10.1016/j.eswa.2008.06.050 · 2009 · External reference
Unresolved reference
External reference
In-sample and out-of-sample Sharpe ratios of multi-factor asset pricing models
10.1016/j.jfineco.2024.103837 · 2024 · External reference
A five-factor asset pricing model
10.1016/j.jfineco.2014.10.010 · 2015 · External reference
Interval time series forecasting: A systematic literature review
10.1002/for.3024 · ExternalCitation · doi-reference
Forecasting interval-valued returns of crude oil: A novel kernel-based approach
10.1002/for.3167 · ExternalCitation · doi-reference
Price discovery in China’s crude oil futures markets: An emerging Asian benchmark?
10.1002/fut.22384 · ExternalCitation · doi-reference
A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
10.1007/bf00344251 · ExternalCitation · doi-reference
Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm
10.1007/s10479-022-04781-6 · ExternalCitation · doi-reference
Statistical analysis of cointegration vectors
10.1016/0165-1889(88)90041-3 · ExternalCitation · doi-reference
A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting
10.1016/j.apenergy.2015.07.025 · ExternalCitation · doi-reference
Forecasting the prices of crude oil using the predictor, economic and combined constraints
10.1016/j.econmod.2018.06.020 · ExternalCitation · doi-reference
Intraday return predictability in China’s crude oil futures market: New evidence from a unique trading mechanism
10.1016/j.econmod.2021.01.005 · ExternalCitation · doi-reference
A generalized pattern matching approach for multi-step prediction of crude oil price
10.1016/j.eneco.2006.10.012 · ExternalCitation · doi-reference
Oil price shocks in a data-rich environment
10.1016/j.eneco.2014.07.006 · ExternalCitation · doi-reference
Forecasting the prices of crude oil: An iterated combination approach
10.1016/j.eneco.2018.01.027 · ExternalCitation · doi-reference
Forecasting the WTI crude oil price by a hybrid-refined method
10.1016/j.eneco.2018.02.004 · ExternalCitation · doi-reference
Interval decomposition ensemble approach for crude oil price forecasting
10.1016/j.eneco.2018.10.015 · ExternalCitation · doi-reference
Global financial uncertainties and China’s crude oil futures market: Evidence from interday and intraday price dynamics
10.1016/j.eneco.2021.105149 · ExternalCitation · doi-reference
Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting
10.1016/j.eneco.2021.105622 · ExternalCitation · doi-reference
Convolutional neural network forecasting of European Union allowances futures using a novel unconstrained transformation method
10.1016/j.eneco.2022.106049 · ExternalCitation · doi-reference
Price discovery efficiency of China’s crude oil futures: Evidence from the Shanghai crude oil futures market
10.1016/j.eneco.2022.106172 · ExternalCitation · doi-reference
Evidence of the internationalization of China’s crude oil futures: Asymmetric linkages to global financial risks
10.1016/j.eneco.2023.107083 · ExternalCitation · doi-reference
Forecasting crude oil futures price using machine learning methods: Evidence from China
10.1016/j.eneco.2023.107089 · ExternalCitation · doi-reference
Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model
10.1016/j.eneco.2024.107588 · ExternalCitation · doi-reference
Good volatility, bad volatility and economic uncertainty: Evidence from the crude oil futures market
10.1016/j.energy.2021.119924 · ExternalCitation · doi-reference
Study on the impacts of Shanghai crude oil futures on global oil market and oil industry based on VECM and DAG models
10.1016/j.energy.2021.120050 · ExternalCitation · doi-reference
Examining the dynamic effect of COVID-19 pandemic on dwindling oil prices using structural vector autoregressive model
10.1016/j.energy.2021.120813 · ExternalCitation · doi-reference
Investor sentiment and machine learning: Predicting the price of China’s crude oil futures market
10.1016/j.energy.2022.123471 · ExternalCitation · doi-reference
Forecasting the crude oil prices with an EMD-ISBM-FNN model
10.1016/j.energy.2022.125407 · ExternalCitation · doi-reference
Modeling crude oil volatility using economic sentiment analysis and opinion mining of investors via deep learning and machine learning models
10.1016/j.energy.2023.130017 · ExternalCitation · doi-reference
How does digital village alleviate rural household energy poverty?
10.1016/j.energy.2025.134713 · ExternalCitation · doi-reference
The impact of climate risks on global energy production and consumption: New evidence from causality-in-quantile and wavelet analysis
10.1016/j.energy.2025.134850 · ExternalCitation · doi-reference
Research on crude oil futures price prediction methods: A perspective based on quantum deep learning
10.1016/j.energy.2025.135080 · ExternalCitation · doi-reference
Extreme risk spillovers between SC, WTI and Brent crude oil futures-Evidence from time-varying Granger causality test
10.1016/j.energy.2025.135495 · ExternalCitation · doi-reference
Hybrid approach to the Japanese candlestick method for f inancial forecasting
10.1016/j.eswa.2008.06.050 · ExternalCitation · doi-reference
CNNpred: CNN-based stock market prediction using a diverse set of variables
10.1016/j.eswa.2019.03.029 · ExternalCitation · doi-reference
China’s crude oil futures: Introduction and some stylized facts
10.1016/j.frl.2018.06.005 · ExternalCitation · doi-reference
The pricing efficiency of crude oil futures in the Shanghai International Exchange
10.1016/j.frl.2019.101329 · ExternalCitation · doi-reference
Quantum-enhanced forecasting: Leveraging quantum gramian angular field and CNNs for stock return predictions
10.1016/j.frl.2024.105840 · ExternalCitation · doi-reference
A novel hybrid forecasting system for crude oil futures prices: A dual perspective of deterministic forecasting and uncertainty analysis
10.1016/j.heliyon.2024.e39818 · ExternalCitation · doi-reference
Text-based crude oil price forecasting: A deep learning approach
10.1016/j.ijforecast.2018.07.006 · ExternalCitation · doi-reference
Do China’s macro-financial factors determine the Shanghai crude oil futures market?
10.1016/j.irfa.2021.101953 · ExternalCitation · doi-reference
The spillover and comovement of downside and upside tail risks among crude oil futures markets
10.1016/j.irfa.2024.103578 · ExternalCitation · doi-reference
The connectedness between crude oil and financial markets: Evidence from implied volatility indices
10.1016/j.jcomm.2016.11.002 · ExternalCitation · doi-reference
Threshold autoregressive models for interval-valued time series data
10.1016/j.jeconom.2018.06.009 · ExternalCitation · doi-reference
A five-factor asset pricing model
10.1016/j.jfineco.2014.10.010 · ExternalCitation · doi-reference
In-sample and out-of-sample Sharpe ratios of multi-factor asset pricing models
10.1016/j.jfineco.2024.103837 · ExternalCitation · doi-reference
China’s Crude oil futures forecasting with search engine data
10.1016/j.procs.2022.11.266 · ExternalCitation · doi-reference
Crude oil future price forecasting using pretrained transformer model
10.1016/j.procs.2024.08.234 · ExternalCitation · doi-reference
The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China
10.1016/j.resourpol.2021.102173 · ExternalCitation · doi-reference
A novel hybrid approach to forecast crude oil futures using intraday data
10.1016/j.techfore.2020.120126 · ExternalCitation · doi-reference
Oil and the Macroeconomy since World War II
10.1086/261140 · ExternalCitation · doi-reference
10.1109/ssci.2017.8285188
10.1109/ssci.2017.8285188 · ExternalCitation · doi-reference
CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization
10.1109/tvcg.2020.3030418 · ExternalCitation · doi-reference
Long Short-Term Memory
10.1162/neco.1997.9.8.1735 · ExternalCitation · doi-reference
A structural VAR and VECM modeling method for open-high-low-close data contained in candlestick chart
10.1186/s40854-024-00622-6 · ExternalCitation · doi-reference
10.1371/journal.pone.0249852
10.1371/journal.pone.0249852 · ExternalCitation · doi-reference
10.1371/journal.pone.0297554
10.1371/journal.pone.0297554 · ExternalCitation · doi-reference
Macroeconomics and reality
10.2307/1912017 · ExternalCitation · doi-reference
Cointegration and Error Correction Representation, Estimation and Testing
10.2307/1913236 · ExternalCitation · doi-reference
10.3389/fenrg.2022.741018
10.3389/fenrg.2022.741018 · ExternalCitation · doi-reference
10.3389/fenvs.2021.636903
10.3389/fenvs.2021.636903 · ExternalCitation · doi-reference
10.3390/en15061955
10.3390/en15061955 · ExternalCitation · doi-reference
Forecasting nonlinear crude oil futures prices
10.5547/issn0195-6574-ej-vol27-no4-4 · ExternalCitation · doi-reference
