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相关概念视频

Regression Analysis01:11

Regression Analysis

5.8K
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:
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Residual Plots01:07

Residual Plots

4.7K
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...
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

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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:
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Prediction Intervals01:03

Prediction Intervals

2.3K
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. 
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相关实验视频

Updated: Jul 23, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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基于多重分解和XGBoost算法的碳价格预测.

Ke Xu1, Zhanguo Xia2, Miao Cheng3

  • 1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.

Environmental science and pollution research international
|July 13, 2023
PubMed
概括
此摘要是机器生成的。

一个新的多分解XGBOOST模型提高了碳价格预测的准确性. 这种方法提高了波动性和稳定的碳市场的预测,为决策者和市场参与者提供了更好的洞察力.

关键词:
在CEEMDAN,你会发现.碳价格预测预测 碳价格预测分解与整合的分解.多重分解算法多重分解算法样本透率 样本透率 样本透率 样本透率在XGBoost上使用.

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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:

  • 碳交易是控制全球二氧化碳排放的关键机制.
  • 碳定价极大地影响了市场参与者和政策制定者的决策.
  • 准确的碳价格预测对于有效的市场管理至关重要.

研究的目的:

  • 提出和评估一个新的碳价格预测模型,名为多分解-XGBOOST.
  • 在不同的市场条件下提高碳价格预测的准确性.
  • 为市场参与者和政策制定者提供一个强大的工具.

主要方法:

  • 使用完整的集体实证模式分解与自适应噪声 (CEEMDAN) 进行初始价格序列分解.
  • 应用变量模式分解 (VMD) 到具有最高样本的内在模式函数 (IMF).
  • 基于样本的重组IMF,随后进行进一步的CEEMDAN分解和XGBoost预测.

主要成果:

  • 多分解XGBOOST模型在北京和湖北碳市场都表现出卓越的预测性能.
  • 在北京市场,与单一的XGBoost.相比,该模型分别提高了RMSE,MAE和MAPE30.4%,44.5%和42.9%,相比之下,单一的XGBoost.
  • 在湖北市场,RMSE,MAE和MAPE分别下降了28.5%,39.4%和39.4%.

结论:

  • 拟议的多分解-XGBOOST模型为碳价格提供了显著改善的预测准确性.
  • 该模型的有效性涵盖了高度波动和相对稳定的碳市场.
  • 这种方法为了解和预测碳市场动态提供了可靠的工具.