Oil price forecasting: A hybrid GRU neural network based on decomposition-reconstruction methods

[1]  Zhiwei Ni,et al.  A Novel Methanol Futures Price Prediction Method Based on Multicycle CNN-GRU and Attention Mechanism , 2022, Arabian Journal for Science and Engineering.

[2]  Kum Fai Yuen,et al.  Newbuilding ship price forecasting by parsimonious intelligent model search engine , 2022, Expert Syst. Appl..

[3]  Zhehao Huang,et al.  Carbon price forecasting based on CEEMDAN and LSTM , 2022, Applied Energy.

[4]  K. Lai,et al.  A CEEMD-ARIMA-SVM model with structural breaks to forecast the crude oil prices linked with extreme events , 2022, Soft Computing.

[5]  Yuze Li,et al.  A New Hybrid VMD-ICSS-BiGRU Approach for Gold Futures Price Forecasting and Algorithmic Trading , 2021, IEEE Transactions on Computational Social Systems.

[6]  Jiani Heng,et al.  A novel multiscale forecasting model for crude oil price time series , 2021, Technological Forecasting and Social Change.

[7]  Wen-Chen Huang,et al.  Constructing a stock-price forecast CNN model with gold and crude oil indicators , 2021, Appl. Soft Comput..

[8]  Jun Wang,et al.  Short-term forecasting of natural gas prices by using a novel hybrid method based on a combination of the CEEMDAN-SE-and the PSO-ALS-optimized GRU network , 2021 .

[9]  Xinsong Niu,et al.  Wind speed forecasting system based on gated recurrent units and convolutional spiking neural networks , 2021 .

[10]  Shengzhi Huang,et al.  A Hybrid VMD-SVM Model for Practical Streamflow Prediction Using an Innovative Input Selection Framework , 2021, Water Resources Management.

[11]  Wanzeng Kong,et al.  Epilepsy prediction through optimized multidimensional sample entropy and Bi-LSTM , 2021, Biomed. Signal Process. Control..

[12]  Li Sun,et al.  Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations , 2020, Renewable Energy.

[13]  Quan Cui,et al.  An innovative random forest-based nonlinear ensemble paradigm of improved feature extraction and deep learning for carbon price forecasting. , 2020, The Science of the total environment.

[14]  Junhui Guo,et al.  Oil Price Forecast Using Deep Learning and ARIMA , 2019, 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI).

[15]  Fan Zhang,et al.  A hybrid VMD-BiGRU model for rubber futures time series forecasting , 2019, Appl. Soft Comput..

[16]  Hemerson Pistori,et al.  Long-term forecast of energy commodities price using machine learning , 2019, Energy.

[17]  Jian Cao,et al.  Financial time series forecasting model based on CEEMDAN and LSTM , 2019, Physica A: Statistical Mechanics and its Applications.

[18]  Ethem Çanakoğlu,et al.  Carbon price forecasting models based on big data analytics , 2019, Carbon Management.

[19]  Jia-Qi Zhu,et al.  Improved EEMD-based crude oil price forecasting using LSTM networks , 2019, Physica A: Statistical Mechanics and its Applications.

[20]  Ting Yao,et al.  Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models? , 2019, International Review of Economics & Finance.

[21]  Purva Raut,et al.  Application of LSTM, GRU and ICA for Stock Price Prediction , 2018, Information and Communication Technology for Intelligent Systems.

[22]  Qiang Wang,et al.  A novel hybridization of nonlinear grey model and linear ARIMA residual correction for forecasting U.S. shale oil production , 2018, Energy.

[23]  Lixin Tian,et al.  A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms , 2018, Applied Energy.

[24]  Ali Safari,et al.  Oil price forecasting using a hybrid model , 2018 .

[25]  Jimin Ye,et al.  Crude oil price analysis and forecasting based on variational mode decomposition and independent component analysis , 2017 .

[26]  M. S. Kiran,et al.  Crude oil price forecasting using XGBoost , 2017, 2017 International Conference on Computer Science and Engineering (UBMK).

[27]  K. P. Soman,et al.  Stock price prediction using LSTM, RNN and CNN-sliding window model , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[28]  Jianping Li,et al.  A deep learning ensemble approach for crude oil price forecasting , 2017 .

[29]  Werner Kristjanpoller,et al.  Forecasting volatility of oil price using an artificial neural network-GARCH model , 2016, Expert Syst. Appl..

[30]  Hongguang Jin,et al.  Performance of a combined cooling heating and power system with mid-and-low temperature solar thermal energy and methanol decomposition integration , 2015 .

[31]  Arun Agarwal,et al.  Recurrent neural network and a hybrid model for prediction of stock returns , 2015, Expert Syst. Appl..

[32]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[33]  Patrick Flandrin,et al.  A complete ensemble empirical mode decomposition with adaptive noise , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[34]  Adnan Khashman,et al.  Intelligent prediction of crude oil price using Support Vector Machines , 2011, 2011 IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI).

[35]  Marcelo Portes Albuquerque,et al.  Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods , 2009, Expert Syst. Appl..

[36]  Lean Yu,et al.  A New Method for Crude Oil Price Forecasting Based on Support Vector Machines , 2006, International Conference on Computational Science.

[37]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[38]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[39]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[40]  M. Zhang,et al.  Using long short-term memory model to study risk assessment and prediction of China’s oil import from the perspective of resilience theory , 2021 .

[41]  Ling Tang,et al.  A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting , 2016, Eng. Appl. Artif. Intell..

[42]  Bing Wang,et al.  Forecasting Crude Oil Price with an Autoregressive Integrated Moving Average (ARIMA) Model , 2014 .

[43]  Xiaopeng Guo,et al.  Improved Support Vector Machine Oil Price Forecast Model Based on Genetic Algorithm Optimization Parameters , 2012 .

[44]  W. Fong,et al.  Chasing trends: recursive moving average trading rules and internet stocks , 2005 .

[45]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .