基于二次分解和多尺度预测的碳价格预测方法
Yonghui Duan1, Kaige Liu2, Xiang Wang3
1Department of Civil Engineering, Henan University of Technology, No. 100, Lianhua St, Gaoxin District, Zhengzhou, 450001, China.
Carbon balance and management
|November 14, 2025
概括
本研究介绍了一种使用二次分解和多尺度预测的混合碳价格预测模型. 这种新的方法显著提高了碳交易市场的准确性,帮助政策和可持续发展.
科学领域:
- 环境经济学环境经济学
- 计算金融是指计算金融.
- 数据科学数据科学数据科学
背景情况:
- 有效的碳价格预测对于稳定的碳交易市场至关重要.
- 现有的模型经常与碳价格的复杂性和波动性作斗争.
研究的目的:
- 开发一种混合碳价格预测模型,以提高准确性.
- 为碳配额定价和市场治理提供强有力的定量支持.
主要方法:
- 一个混合模型,将WOA-XGBoost用于初始预测和CEEMDAN-VMD用于二次分解.
- 多尺度预测包括高频和低频组件的不同解释变量.
- 基于频率特征的残留分解和组件重组.
主要成果:
- 拟议的混合模型在湖北和欧盟碳市场 (MAE < 0.0013,R2 = 0.9999) 中明显优于基准模型.
- 历史碳价格是主要的驱动因素,百度指数 (湖北) 和德国达克斯指数 (欧盟) 显著影响.
结论:
- 混合模型提供了高精度的碳价格预测.
- 该框架支持完善碳市场治理,政策评估和全球减排努力.
- 它通过更好的市场管理促进绿色和可持续的发展.
相关概念视频
01:11Regression Analysis
7.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.7K
01:23Quadratic Models
157
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
157
01:03Prediction Intervals
3.1K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.1K
02:35Clausius-Clapeyron Equation
62.2K
The equilibrium between a liquid and its vapor depends on the temperature of the system; a rise in temperature causes a corresponding rise in the vapor pressure of its liquid. The Clausius-Clapeyron equation gives the quantitative relation between a substance’s vapor pressure (P) and its temperature (T); it predicts the rate at which vapor pressure increases per unit increase in temperature.
62.2K
02:52Predicting Products: Substitution vs. Elimination
13.7K
When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
The following factors can influence the mechanisms competing against each other:
13.7K
01:07Residual Plots
6.0K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
When the residual values are plotted against the variable x, it is called a residual...
6.0K