Forecasting Regional Carbon Prices in China Based on Secondary Decomposition and a Hybrid Kernel-Based Extreme Learning Machine
Accurately forecasting carbon prices is key to managing associated risks in the financial market for carbon. To this end, the traditional strategy does not adequately decompose carbon prices, and the kernel extreme learning machine (KELM) with a single kernel function struggles to adapt to the nonli...
Main Authors: | Yunhe Cheng, Beibei Hu |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-05-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/15/10/3562 |
Similar Items
-
A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors
by: Jianguo Zhou, et al.
Published: (2021-03-01) -
Short-Term Electricity Price Forecasting Based on the Two-Layer VMD Decomposition Technique and SSA-LSTM
by: Fang Guo, et al.
Published: (2022-11-01) -
Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine
by: Sajjad Khan, et al.
Published: (2021-06-01) -
A New Hybrid Cryptocurrency Returns Forecasting Method Based on Multiscale Decomposition and an Optimized Extreme Learning Machine Using the Sparrow Search Algorithm
by: Xiaoxu Du, et al.
Published: (2022-01-01) -
Forecasting of Steam Coal Price Based on Robust Regularized Kernel Regression and Empirical Mode Decomposition
by: Xiangwan Fu, et al.
Published: (2021-10-01)