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相关概念视频

Regression Analysis01:11

Regression Analysis

5.6K
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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The Carbon Cycle01:14

The Carbon Cycle

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Carbon is the basis of all organic matter on Earth, and is recycled through the ecosystem in two primary processes: one in which carbon is exchanged among living organisms, and one in which carbon is cycled over long periods of time through fossilized organic remains, weathering of rocks, and volcanic activity. Human activities, including increased agricultural practices and the burning of fossil fuels, has greatly affected the balance of the natural carbon cycle.
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Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

39
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
39
Prediction Intervals01:03

Prediction Intervals

2.2K
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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¹³C NMR: ¹H–¹³C Decoupling01:04

¹³C NMR: ¹H–¹³C Decoupling

1.0K
The probability of having two carbon-13 atoms next to each other is negligible because of the low natural abundance of carbon-13. Consequently, peak splitting due to carbon-carbon spin-spin coupling is not observed in spectra. However, protons up to three sigma bonds away split the carbon signal according to the n+1 rule, resulting in complicated spectra.
A broadband decoupling technique is used to simplify these complex, sometimes overlapping, signals. Broadband decoupling relies on a...
1.0K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

462
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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相关实验视频

Updated: Jun 3, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
10:22

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

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一种用于预测碳价格的双分解集成和错误校正模型.

Yanan Li1, Xinsheng Zhang1, Minghu Wang1

  • 1School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China.

Journal of environmental management
|January 11, 2025
PubMed
概括

本研究介绍了一种使用双分解和错误校正的先进碳价格预测模型. 该模型显著提高了碳市场的预测准确性,使决策者和利益相关者受益.

科学领域:

  • 环境经济学环境经济学
  • 计算金融是指计算金融.
  • 时间序列分析时间序列分析

背景情况:

  • 碳价格预测对于市场稳定和政策至关重要,但由于复杂,非线性市场动态,它具有挑战性.
  • 现有的模型经常与碳价格的固有不稳定性和多因素影响作斗争.
  • 准确的预测对于有效的气候变化减缓战略和碳市场运作至关重要.

研究的目的:

  • 开发和验证一种新的碳价格预测模型,通过整合双分解和错误纠正技术来提高准确性.
  • 解决现有模型在捕捉碳价格波动的复杂动态方面的局限性.
  • 为参与碳市场和气候政策的利益相关者提供更可靠的工具.

主要方法:

  • 将碳价格序列分解为内在模式函数 (IMFs),使用由子搜索算法 (SVMD) 优化的变化模式分解.
  • 通过使用模糊的复杂性对IMF进行分类,然后使用鱼优化算法优化的长短期记忆网络 (WLSTM) 进行复杂的IMF和极端学习机器 (ELM) 进行简单的IMF的预测.
  • 通过预测错误的整体实证模式分解 (EEMD) 来纠正错误,然后重建初始和错误预测.

主要成果:

  • 与15个基准模型相比,拟议的模型在关键指标 (RMSE,MAE,MAPE,R2) 中表现优越.
  • 绩效指标的平均改善率至少达到19.89% (RMSE),25.11% (MAE),25.01% (MAPE) 和0.79% (R2).这些指标的平均改善率至少为19.89% (RMSE),25.11% (MAE),25.01% (MAPE) 和0.79% (R2).
关键词:
碳价格预测预测 碳价格预测将整合分解成一个整合.纠正错误 纠正错误 纠正错误 纠正错误模糊的是什么?模糊的是什么?长期短期内存网络中的长期内存.变化模式分解的变化模式分解

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  • 使用来自中国碳交易所的真实数据进行验证证实了该模型的有效性和稳定性.

    • 结论:

    • 集成的双分解和错误校正模型在碳价格预测准确度方面取得了重大进展.
    • 该方法有效地处理碳市场数据固有的非线性和不稳定性.
    • 这些发现为加强碳市场决策和支持气候政策提供了有价值的工具.