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Deep learning framework for predictive modeling of crude oil price for sustainable management in oil markets
16 references·3 citing·44 second hop
Deep learning framework for predictive modeling of crude oil price for sustainable management in oil markets (2022)Deep learning framework for predictive mo…Carbon price forecasting based on CEEMDAN and LSTM (2022)Carbon price forecasting base…Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm (2021)Multi-step-ahead crude oil pr…Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization (2021)Crude oil time series predict…Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance (2021)Crude oil price prediction: A…Constructing a stock-price forecast CNN model with gold and crude oil indicators (2021)Constructing a stock-price fo…A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting (2021)A combined architecture of mu…Stock price prediction using deep learning and frequency decomposition (2020)Stock price prediction using …A novel hybrid model for forecasting crude oil price based on time series decomposition (2020)A novel hybrid model for fore…A new hybrid model for forecasting Brent crude oil price (2020)A new hybrid model for foreca…Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network (2019)Non-ferrous metals price fore…Interval decomposition ensemble approach for crude oil price forecasting (2018)Interval decomposition ensemb…A deep learning ensemble approach for crude oil price forecasting (2017)A deep learning ensemble appr…Forecasting Nonlinear Crude Oil Futures Prices (2006)Forecasting Nonlinear Crude O…A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1997)A Decision-Theoretic Generali…Comparing Predictive Accuracy (1995)Comparing Predictive AccuracyLikelihood Ratio Statistics for Autoregressive Time Series with a Unit Root (1981)Likelihood Ratio Statistics f…Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models (2023)Novel deterministic and proba…Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods (2023)Forecasting the lithium miner…A dynamic clustering ensemble learning approach for crude oil price forecasting (2023)A dynamic clustering ensemble…Time Series Analysis: Forecasting and Control (1977)Distribution of the Estimators for Autoregressive Time Series with a Unit Root (1979)Bounds testing approaches to the analysis of level relationships (2001)Unit root tests in panel data: asymptotic and finite-sample properties (2002)Testing for a Unit Root in Time Series Regression (1988)Comparing Predictive Accuracy (1994)Stock price prediction based on deep neural networks (2019)A comprehensive evaluation of ensemble learning for stock-market prediction (2020)Price forecasting through neural networks for crude oil, heating oil, and natural gas (2024)Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008)Improvement of Auto-Regressive Integrated Moving Average models using Fuzzy logic and Artificial Neural Networks (ANNs) (2008)A hybrid stock price index forecasting model based on variational mode decomposition and LSTM network (2020)A new crude oil price forecasting model based on variational mode decomposition (2020)Predictions of steel price indices through machine learning for the regional northeast Chinese market (2024)Efficient Capital Markets: A Review of Theory and Empirical Work (1970)EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* (1970)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022 (2023)Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm (2023)TRNN: An efficient time-series recurrent neural network for stock price prediction (2023)The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis (1989)Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method (2009)Crude oil price analysis and forecasting using wavelet decomposed ensemble model (2012)A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms (2018)Forecasting with artificial neural networks: (1998)An Empirical Comparison of Machine Learning Models for Time Series Forecasting (2010)A Comparative Study of Demand Forecasting Models for a Multi-Channel Retail Company: A Novel Hybrid Machine Learning Approach (2022)Predicting the changes in the WTI crude oil price dynamics using machine learning models (2022)A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, and statistical models (2023)Technical analysis of the financial markets: a comprehensive guide to trading methods and applications (1999)Time series forecasting of petroleum production using deep LSTM recurrent networks (2018)How Can China Achieve the 2030 Carbon Peak Goal—A Crossover Analysis Based on Low-Carbon Economics and Deep Learning (2022)Forecasting commodity prices: empirical evidence using deep learning tools (2023)A new deep neural network for forecasting: Deep dendritic artificial neural network (2024)Time Series Analysis Forecasting and Control. (1971)Testing the equality of prediction mean squared errors (1997)Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM (2022)Oil price forecasting: A hybrid GRU neural network based on decomposition–reconstruction methods (2023)A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China (2024)The Nonstochastic Multiarmed Bandit Problem (2002)Prediction, Learning, and Games (2006)Survey on speech emotion recognition: Features, classification schemes, and databases (2010)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

  • 1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting20,469 cited
  • 1981Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root14,647 cited
  • 1995Comparing Predictive Accuracy5,214 cited
  • 2022Carbon price forecasting based on CEEMDAN and LSTM424 cited
  • 2017A deep learning ensemble approach for crude oil price forecasting383 cited
  • 2020Stock price prediction using deep learning and frequency decomposition373 cited
  • 2021Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization256 cited
  • 2019Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network254 cited
  • 2006Forecasting Nonlinear Crude Oil Futures Prices176 cited
  • 2021Crude oil price prediction: A comparison between AdaBoost-LSTM and AdaBoost-GRU for improving forecasting performance170 cited
  • 2020A novel hybrid model for forecasting crude oil price based on time series decomposition137 cited
  • 2020A new hybrid model for forecasting Brent crude oil price133 cited
  • 2021Constructing a stock-price forecast CNN model with gold and crude oil indicators129 cited
  • 2018Interval decomposition ensemble approach for crude oil price forecasting120 cited

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  • 2021A combined architecture of multivariate LSTM with Mahalanobis and Z-Score transformations for oil price forecasting102 cited
  • 2021Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm101 cited
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    • 2023Forecasting the lithium mineral resources prices in China: Evidence with Facebook Prophet (Fb-P) and Artificial Neural Networks (ANN) methods48 cited
    • 2023Novel deterministic and probabilistic forecasting methods for crude oil price employing optimized deep learning, statistical and hybrid models44 cited
    • 2023A dynamic clustering ensemble learning approach for crude oil price forecasting40 cited