Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Regression Analysis01:11

Regression Analysis

5.7K
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:
5.7K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

345
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
345
Maxwell-Boltzmann Distribution: Problem Solving01:20

Maxwell-Boltzmann Distribution: Problem Solving

1.5K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.5K
Residual Plots01:07

Residual Plots

4.6K
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...
4.6K
Survival Tree01:19

Survival Tree

88
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...
88
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. 
2.3K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Study on TPD Phasemeter to Suppress Low-Frequency Amplitude Fluctuation and Improve Fast-Acquiring Range for GW Detection.

Sensors (Basel, Switzerland)·2024
Same author

Combination of acute intermittent hypoxia and intermittent transcutaneous electrical stimulation in obstructive sleep apnea: a randomized controlled crossover trial.

Respiratory physiology & neurobiology·2024
Same author

Detection of Reactive Oxygen Species in Plant Root Immunity.

Methods in molecular biology (Clifton, N.J.)·2024
Same author

Genomic characterization and resistance features of Streptococcus agalactiae isolated from non-pregnant adults in Shandong, China.

Journal of global antimicrobial resistance·2024
Same author

Practice effects of a breathing technique on pilots' cognitive and stress associated heart rate variability during flight operations.

Stress (Amsterdam, Netherlands)·2024
Same author

Penile prosthesis implantation: a bibliometric-based visualization study.

International journal of impotence research·2024

Related Experiment Video

Updated: Jul 12, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.6K

Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf

Mengdan Feng1, Yonghui Duan2, Xiang Wang3

  • 1Department of Civil Engineering, Henan University of Technology, No. 100, Lianhua Street, Gaoxin District, Zhengzhou, 450001, China. mengdanfeng139499@foxmail.com.

Scientific Reports
|October 27, 2023
PubMed
Summary

Accurate carbon price prediction is vital for reducing CO2 emissions. A novel hybrid model combining Grey Wolf Optimizer (GWO), Extreme Gradient Boosting (XGBOOST), and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) significantly improves forecasting accuracy.

More Related Videos

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Related Experiment Videos

Last Updated: Jul 12, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
10:36

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption

Published on: November 3, 2023

1.6K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.1K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K

Area of Science:

  • Environmental Science
  • Computational Economics
  • Machine Learning

Background:

  • Precise carbon price prediction is crucial for effective CO2 emission reduction and global warming mitigation.
  • Single machine learning models often exhibit limitations in forecasting carbon prices due to inherent complexities.
  • Identifying key carbon price indicators is essential for developing robust predictive models.

Purpose of the Study:

  • To propose a novel hybrid carbon price prediction model (GWO-XGBOOST-CEEMDAN) that overcomes the limitations of single models.
  • To enhance the accuracy and reliability of carbon price forecasting.
  • To provide an effective method for predicting future carbon prices in emission trading markets.

Main Methods:

  • Utilized Random Forest (RF) for screening primary carbon price indicators and identifying influencing factors.
  • Developed a Grey Wolf Optimizer (GWO) optimized Extreme Gradient Boosting (XGBOOST) model (GWO-XGBOOST).
  • Applied Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to decompose and correct residuals from the GWO-XGBOOST model, creating the GWO-XGBOOST-CEEMDAN model.

Main Results:

  • The proposed GWO-XGBOOST-CEEMDAN model demonstrated enhanced prediction precision in experimental forecasts.
  • The hybrid model outperformed comparison models in predicting carbon prices across Guangdong, Hubei, and Fujian emission trading markets.
  • The study validated the effectiveness of the integrated GWO, XGBOOST, and CEEMDAN approach for carbon price forecasting.

Conclusions:

  • The GWO-XGBOOST-CEEMDAN model offers a superior approach to carbon price prediction compared to traditional methods.
  • The hybrid model provides a reliable and effective experimental method for forecasting future carbon prices.
  • Accurate carbon price prediction using advanced machine learning techniques is vital for climate change mitigation efforts.