Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Regression Analysis

Regression Analysis

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:

Multiple Regression

Multiple Regression

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...

Prediction Intervals

Prediction Intervals

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.

Multicompartment Models: Overview

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...

You might also read

Related Articles

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

Sort by

Same author

Tensile strain switched ferromagnetism in layered NbS2 and NbSe2.

ACS nano·2012

Same author

Synthesis of near-infrared-emitting CdTeSe and CdZnTeSe quantum dots.

Luminescence : the journal of biological and chemical luminescence·2012

Same author

Renal tolerability of iopromide and iodixanol in 562 renally impaired patients undergoing cardiac catheterisation: the DIRECT study.

EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology·2012

Same author

Removal of FePO4 and Fe3(PO4)2 crystals on the surface of passive fillers in Fe0/GAC reactor using the acclimated bacteria.

Journal of hazardous materials·2012

Same author

Decorating PtCo bimetallic alloy nanoparticles on graphene as sensors for glucose detection by catalyzing luminol chemiluminescence.

Small (Weinheim an der Bergstrasse, Germany)·2012

Same author

Photoluminescent enhancement of CdSe/Cd(1-x) Zn(x)S quantum dots by hexadecylamine at room temperature.

Journal of nanoscience and nanotechnology·2012

Search research articles

Related Experiment Video

Updated: Sep 2, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

A carbon price hybrid forecasting model based on data multi-scale decomposition and machine learning.

Ping Yang¹, Yelin Wang¹, Shunyu Zhao¹

¹Faculty of Management and Economics, Kunming University of Science and Technology, Kunming, Yunnan, 650093, People's Republic of China.

Environmental Science and Pollution Research International

|August 9, 2022

Summary

Accurate carbon price forecasting is challenging due to market volatility. A new hybrid model, CEEMDAN-PE-LSTM-RVM, improves prediction accuracy and reliability for carbon financial markets.

Keywords:

Carbon price forecasting Chaos theory Complete ensemble empirical mode decomposition with adaptive noise Permutation entropy Time series analysis

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Related Experiment Videos

Last Updated: Sep 2, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

Environmental Economics
Financial Forecasting
Machine Learning

Background:

Accurate carbon price forecasting is crucial for carbon financial market operations.
Non-linearity and non-stationarity of carbon prices hinder reliable predictions.

Purpose of the Study:

To propose a novel hybrid model for accurate and reliable carbon price forecasting.
To address the limitations of existing models in terms of applicability and accuracy.

Main Methods:

A hybrid model named CEEMDAN-PE-LSTM-RVM was developed, integrating decomposition, entropy, and machine learning.
The model utilizes a prediction-under-classification structure and introduces chaos degree as a quantitative feature.
Empirical evaluation was conducted using historical data from four representative carbon prices.

Main Results:

The proposed CEEMDAN-PE-LSTM-RVM model achieved average MAPE of 1.7027 and RMSE of 0.7993.
Performance significantly outperformed other models, demonstrating high accuracy and reliability.
The model exhibited robustness, with reduced sensitivity to the complexity of predicted carbon price data.

Conclusions:

The CEEMDAN-PE-LSTM-RVM model offers a reliable tool for carbon financial markets.
The study provides a benchmark for assessing prediction reliability using chaos degree.
The model's performance highlights its effectiveness in handling complex carbon price dynamics.