Carbon price forecasting with variational mode decomposition and optimal combined model

2019 ◽  
Vol 519 ◽  
pp. 140-158 ◽  
Author(s):  
Jiaming Zhu ◽  
Peng Wu ◽  
Huayou Chen ◽  
Jinpei Liu ◽  
Ligang Zhou
Keyword(s):  
Carbon Price ◽  
Price Forecasting ◽  
Variational Mode Decomposition ◽  
Combined Model ◽  
Mode Decomposition

scholarly journals Multi-Step-Ahead Carbon Price Forecasting Based on Variational Mode Decomposition and Fast Multi-Output Relevance Vector Regression Optimized by the Multi-Objective Whale Optimization Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 147 ◽  
Author(s):  
Shenghua Xiong ◽  
Chunfeng Wang ◽  
Zhenming Fang ◽  
Dan Ma
Keyword(s):  
Optimization Algorithm ◽  
Carbon Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Variational Mode Decomposition ◽  
Forecasting Accuracy ◽  
Mode Decomposition ◽  
Whale Optimization ◽  
Relevance Vector Regression ◽  
Accuracy And Stability

The accurate and stable forecasting of carbon prices is vital for governors to make policies and essential for market participants to make investment decisions, which is important for promoting the development of carbon markets and reducing carbon emissions in China. However, it is challenging to improve the carbon price forecasting accuracy due to its non-linearity and non-stationary characteristics, especially in multi-step-ahead forecasting. In this paper, a hybrid multi-step-ahead forecasting model based on variational mode decomposition (VMD), fast multi-output relevance vector regression (FMRVR), and the multi-objective whale optimization algorithm (MOWOA) is proposed. VMD is employed to extract the primary mode for the carbon price. Then, FMRVR, which is used as the forecasting module, is built on the preprocessed data. To achieve high accuracy and stability, the MOWOA is utilized to optimize the kernel parameter and input the lag of the FMRVR. The proposed hybrid forecasting model is applied to carbon price series from three major regional carbon emission exchanges in China. Results show that the proposed VMD-FMRVR-MOWOA model achieves better performance compared to several other multi-output models in terms of forecasting accuracy and stability. The proposed model can be a potential and effective technique for multi-step-ahead carbon price forecasting in China’s three major regional emission exchanges.

scholarly journals A Carbon Price Forecasting Model Based on Variational Mode Decomposition and Spiking Neural Networks

Energies ◽  
2016 ◽  
Vol 9 (1) ◽  
pp. 54 ◽  
Author(s):  
Guoqiang Sun ◽  
Tong Chen ◽  
Zhinong Wei ◽  
Yonghui Sun ◽  
Haixiang Zang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Carbon Price ◽  
Spiking Neural Networks ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Variational Mode Decomposition ◽  
Model Based ◽  
Mode Decomposition
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