Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In light of the complex characteristics of the regional carbon price in China, this paper proposes a model to forecast carbon price based on the multi-factor hybrid kernel-based extreme learning machine (HKELM) by combining secondary decomposition and ensemble learning. Variational mode decomposition (VMD) is first used to decompose the carbon price into several modes, and range entropy is then used to reconstruct these modes. The multi-factor HKELM optimized by the sparrow search algorithm is used to forecast the reconstructed subsequences, where the main external factors innovatively selected by maximum information coefficient and historical t...
Accurate prediction of the carbon trading price (CTP) is crucial to the decision-making of relevant ...
Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing m...
Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlinear...
Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In ...
Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In ...
Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In ...
Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In ...
Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In ...
Carbon trading is a significant mechanism created to control carbon emissions, and the increasing en...
Accurately predicting the carbon price sequence is important and necessary for promoting the develop...
In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical ...
In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode d...
Numerous studies show that it is reasonable and effective to apply decomposition technology to deal ...
Accurate forecasting of carbon price is important and fundamental for anticipating the changing tren...
With the widespread attention of governments around the world on climate issues, carbon pricing-rela...
Accurate prediction of the carbon trading price (CTP) is crucial to the decision-making of relevant ...
Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing m...
Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlinear...
Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In ...
Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In ...
Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In ...
Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In ...
Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In ...
Carbon trading is a significant mechanism created to control carbon emissions, and the increasing en...
Accurately predicting the carbon price sequence is important and necessary for promoting the develop...
In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical ...
In this study, a novel multiscale nonlinear ensemble leaning paradigm incorporating empirical mode d...
Numerous studies show that it is reasonable and effective to apply decomposition technology to deal ...
Accurate forecasting of carbon price is important and fundamental for anticipating the changing tren...
With the widespread attention of governments around the world on climate issues, carbon pricing-rela...
Accurate prediction of the carbon trading price (CTP) is crucial to the decision-making of relevant ...
Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing m...
Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlinear...