Carbon Trading Price Prediction of Three Carbon Trading Markets in China Based on a Hybrid Model Combining CEEMDAN, SE, ISSA, and MKELM

Abstract

Carbon trading has been deemed as the most effective mechanism to mitigate carbon emissions. However, during carbon trading market operation, competition among market participants will inevitably occur; hence, the precise forecasting of the carbon trading price (CTP) has become a significant element in the formulation of competition strategies. This investigation has established a hybrid CTP forecasting framework combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE) method, improved salp swarm algorithm (ISSA), and multi-kernel extreme learning machine (MKELM) methods to improve forecasting accuracy. Firstly, the initial CTP data sequence is disintegrated into several intrinsic mode functions (IMFs) and a residual sequence by a CEEMDAN method. Secondly, to save calculation time, SE method has been utilized to reconstruct the IMFs and the residual sequence into new IMFs. Thirdly, the new IMFs are fed into the MKELM model, combing RBF and the poly kernel functions to utilize their superior learning and generalization abilities. The parameters of the MKELM model are optimized by ISSA, combining dynamic inertia weight and chaotic local searching method into the SSA to enhance the searching speed, convergence precision, as well as the global searching ability. CTP data in Guangdong, Shanghai, and Hubei are selected to prove the validity of the established CEEMDAN-SE-ISSA-MKELM model. Through a comparison analysis, the established CEEMDAN-SE-ISSA-MKELM model performs the best with the smallest MAPE and RMSE values and the highest R2 value, which are 0.76%, 0.53, and 0.99, respectively, for Guangdong,. Thus, the presented model would be extensively applied in CTP forecasting in the future.

Analysis and forecasting of the dynamics of fuel trade between Turkmenistan and China using the autoregressive integrated moving average (ARIMA) model

“…An important part of modeling the autoregressive integrated moving average (ARIMA) model, which is widely used in forecasting, is parameter estimation. The ADF coefficient test is a fully parametric way to find a unit root in ARIMA models where the order is unknown [19] . In addition, the ARIMA parameter model is useful for analyzing and predicting electrical signals in plant environments, providing information on stability, and predicting short-term trends [20] .…”

A Hybrid Model for Carbon Price Forecasting Based on Improved Feature Extraction and Non-Linear Integration

“…Therefore, more flexible and accurate forecasting methods need to be introduced. Machine learning models predominantly consist of extreme learning machine (ELM) [16][17][18][19], random forest (RF) [3,20,21], and support vector machine (SVM) [22][23][24]. Machine learning models have the advantage of being interpretable and transparent, but their ability to deal with non-linear and non-stationary time series is still inadequate [13].…”