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Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting
16 references·0 citing·50 second hop
Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting (2024)Predicting energy prices based on a novel…Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM (2022)Forecasting gold price using …Forecasting stock index price using the CEEMDAN-LSTM model (2021)Forecasting stock index price…A hybrid model for carbon price forecastingusing GARCH and long short-term memory network (2021)A hybrid model for carbon pri…Multistep-ahead forecasting of coal prices using a hybrid deep learning model (2020)Multistep-ahead forecasting o…An innovative random forest-based nonlinear ensemble paradigm of improved feature extraction and deep learning for carbon price forecasting (2020)An innovative random forest-b…A hybrid VMD–BiGRU model for rubber futures time series forecasting (2019)A hybrid VMD–BiGRU model for …Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis (2018)Hybrid Variational Mode Decom…Financial time series forecasting model based on CEEMDAN and LSTM (2018)Financial time series forecas…An innovative neural network approach for stock market prediction (2018)An innovative neural network …Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations (2016)Forecasting energy market ind…Gold price volatility: A forecasting approach using the Artificial Neural Network–GARCH model (2015)Gold price volatility: A fore…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…Forward curves, scarcity and price volatility in oil and natural gas markets (2009)Forward curves, scarcity and …Comparing Predictive Accuracy (1995)Comparing Predictive AccuracyFitting autoregressive models for prediction (1969)Fitting autoregressive models…Designing a neural network for forecasting financial and economic time series (1996)Forecasting with artificial neural networks: (1998)Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm (2008)Wavelet-based evidence of the impact of oil prices on stock returns (2013)A novel hybrid method for crude oil price forecasting (2015)Forecasting: theory and practice (2022)Evaluating multiple classifiers for stock price direction prediction (2015)Predicting the Price of Bitcoin Using Machine Learning (2018)Deep Learning for Stock Market Prediction (2020)Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis (2020)Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN) (2021)Application of support vector machines in financial time series forecasting (2001)Introduction to Time Series and Forecasting (2002)Forecasting Volatility in Financial Markets: A Review (2003)Stock price prediction using deep learning and frequency decomposition (2020)A graph-based CNN-LSTM stock price prediction algorithm with leading indicators (2021)Carbon price forecasting based on CEEMDAN and LSTM (2022)Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation (1982)Generalized autoregressive conditional heteroskedasticity (1986)Dynamic Conditional Correlation (2002)Forecasting volatility of oil price using an artificial neural network-GARCH model (2016)Peanut oil price change forecasts through the neural network (2025)Modeling the price dynamics of CO2 emission allowances (2008)Predicting stock market index using fusion of machine learning techniques (2014)A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction (2021)A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms (2021)Oil price forecasting: A hybrid GRU neural network based on decomposition–reconstruction methods (2023)Comparing Predictive Accuracy (1994)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)Predictions of steel price indices through machine learning for the regional northeast Chinese market (2024)Thermal coal futures trading volume predictions through the neural network (2024)Forecasting stock market movement direction with support vector machine (2004)Deep learning with long short-term memory networks for financial market predictions (2017)Stock price prediction using LSTM, RNN and CNN-sliding window model (2017)Integration of genetic algorithm with artificial neural network for stock market forecasting (2021)Forecasting crude oil futures prices using BiLSTM-Attention-CNN model with Wavelet transform (2022)Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition (2022)A forecast comparison of volatility models: does anything beat a GARCH(1,1)? (2005)The Performance of LSTM and BiLSTM in Forecasting Time Series (2019)A new decomposition ensemble model for stock price forecasting based on system clustering and particle swarm optimization (2022)A novel deep learning carbon price short-term prediction model with dual-stage attention mechanism (2023)Forecasting carbon price in China unified carbon market using a novel hybrid method with three-stage algorithm and long short-term memory neural networks (2023)A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle (1989)Combining forecasts: A review and annotated bibliography (1989)Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation (1991)Testing the equality of prediction mean squared errors (1997)Consumption, Aggregate Wealth, and Expected Stock Returns (2001)Factor Analysis and AIC (1987)Tourism as a long-run economic growth factor: the Spanish case (2002)Estimation of Panel Vector Autoregression in Stata (2016)
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References

  • 1995Comparing Predictive Accuracy5,370 cited
  • 1969Fitting autoregressive models for prediction2,483 cited
  • 2018Financial time series forecasting model based on CEEMDAN and LSTM712 cited
  • 2018An innovative neural network approach for stock market prediction476 cited
  • 2021A hybrid model for carbon price forecastingusing GARCH and long short-term memory network316 cited
  • 2011Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling282 cited
  • 2015Gold price volatility: A forecasting approach using the Artificial Neural Network–GARCH model219 cited
  • 2022Forecasting gold price using a novel hybrid model with ICEEMDAN and LSTM-CNN-CBAM206 cited
  • 2020An innovative random forest-based nonlinear ensemble paradigm of improved feature extraction and deep learning for carbon price forecasting202 cited
  • 2021Forecasting stock index price using the CEEMDAN-LSTM model189 cited
  • 2011A nonparametric GARCH model of crude oil price return volatility183 cited
  • 2018Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis156 cited
  • 2009Forward curves, scarcity and price volatility in oil and natural gas markets143 cited
  • 2016Forecasting energy market indices with recurrent neural networks: Case study of crude oil price fluctuations

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142 cited
  • 2019A hybrid VMD–BiGRU model for rubber futures time series forecasting140 cited
  • 2020Multistep-ahead forecasting of coal prices using a hybrid deep learning model135 cited
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