A novel decomposition integration model for power coal price forecasting
Tài liệu tham khảo
Abualigah, 2021, The arithmetic optimization algorithm[J], Comput. Methods Appl. Mech. Eng., 376, 10.1016/j.cma.2020.113609
Alameer, 2020, Multistep-ahead forecasting of coal prices using a hybrid deep learning model[J], Resour. Pol., 65, 10.1016/j.resourpol.2020.101588
Bai, 2018
Bonita, 2018, 147
Chen, 2020, Probabilistic forecasting with temporal convolutional neural network[J], Neurocomputing, 399, 491, 10.1016/j.neucom.2020.03.011
Ding, 2021, Probability density forecasts for steam coal prices in China: the role of high-frequency factors[J], Energy, 220, 10.1016/j.energy.2021.119758
Dong, 2021, Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach[J], Sci. Total Environ., 799, 10.1016/j.scitotenv.2021.149509
Dragomiretskiy, 2013, Variational mode decomposition[J], IEEE Trans. Signal Process., 62, 531, 10.1109/TSP.2013.2288675
Fan, 2016, Predicting chaotic coal prices using a multi-layer perceptron network model[J], Resour. Pol., 50, 86, 10.1016/j.resourpol.2016.08.009
Guo, 2016, Coal price forecasting and structural analysis in China[J], Discrete Dynam Nat. Soc., 2016, 1
Han, 2019, Forecasting carbon prices in the Shenzhen market, China: the role of mixed-frequency factors[J], Energy, 171, 69, 10.1016/j.energy.2019.01.009
Herrera, 2019, Long-term forecast of energy commodities price using machine learning[J], Energy, 179, 214, 10.1016/j.energy.2019.04.077
Jiang, 2018, ARIMA forecasting of China’s coal consumption, price and investment by 2030[J], Energy Sources B Energy Econ. Plann., 13, 190, 10.1080/15567249.2017.1423413
Jiang, 2021, Forecasting hourly PM2. 5 based on deep temporal convolutional neural network and decomposition method[J], Appl. Soft Comput., 113, 10.1016/j.asoc.2021.107988
Li, 2019, Efficient lidar signal denoising algorithm using variational mode decomposition combined with a whale optimization algorithm[J], Rem. Sens., 11, 126, 10.3390/rs11020126
Li, 2019, The roles of inter-fuel substitution and inter-market contagion in driving energy prices: evidences from China’s coal market[J], Energy Econ., 84, 10.1016/j.eneco.2019.104525
Li, 2021, How alternative energy competition shocks natural gas development in China: a novel time series analysis approach[J], Resour. Pol., 74, 10.1016/j.resourpol.2021.102409
Liu, 2013, Market-driven coal prices and state-administered electricity prices in China[J], Energy Econ., 40, 167, 10.1016/j.eneco.2013.05.021
Lyu, 2022, Utilization of resources in abandoned coal mines for carbon neutrality[J], Sci. Total Environ., 822, 10.1016/j.scitotenv.2022.153646
Matyjaszek, 2019, Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory[J], Resour. Pol., 61, 283, 10.1016/j.resourpol.2019.02.017
Ming, 2016, Prediction of China’s coal price during Twelfth Five Year Plan period[J, Energy Sources B Energy Econ. Plann., 11, 511, 10.1080/15567249.2012.689797
Shi, 2021, China's coal consumption forecasting using adaptive differential evolution algorithm and support vector machine[J], Resour. Pol., 74
Wang, 2022, Frontiers in environmental science a study on China coal price forecasting based on CEEMDAN-GWO-CatBoost hybrid forecasting model under carbon neutral target[J], Front. Environ. Sci., 10
Wen, 2022, The energy, environment and economy impact of coal resource tax, renewable investment, and total factor productivity growth[J], Resour. Pol., 77, 10.1016/j.resourpol.2022.102742
Yang, 2019, The drivers of coal overcapacity in China: An empirical study based on the quantitative decomposition[J], Resour. Conserv. Recycl., 141, 123, 10.1016/j.resconrec.2018.10.016
Yeh, 2019, 1
Zhang, 2022, A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms[J], Appl. Energy, 306, 10.1016/j.apenergy.2021.118011
Zhao, 2016, Multi-fractal fluctuation features of thermal power coal price in China[J], Energy, 117, 10, 10.1016/j.energy.2016.10.081