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Related Experiment Video

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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Carbon price forecasting using multiscale nonlinear integration model coupled optimal feature reconstruction with

Jujie Wang1,2, Qian Cheng3, Xin Sun3

  • 1School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China. jujiewang@126.com.

Environmental Science and Pollution Research International
|August 28, 2021
PubMed
Summary

This study introduces a novel multiscale nonlinear integration model for accurate carbon price forecasting. The advanced model enhances prediction accuracy, benefiting investors and emission reduction efforts.

Keywords:
Biphasic deep learningCarbon price forecastingMultiscale decompositionNonlinear integrationOptimal feature reconstruction

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Area of Science:

  • Environmental Economics
  • Data Science
  • Machine Learning

Background:

  • Accurate carbon price forecasting is crucial for investors and policymakers.
  • Traditional forecasting methods struggle with the nonlinear and non-stationary nature of carbon prices.
  • Improved forecasting supports energy conservation and emission reduction strategies.

Purpose of the Study:

  • To propose an innovative multiscale nonlinear integration model for enhanced carbon price forecasting.
  • To address the limitations of traditional methods in handling carbon price volatility.
  • To improve the accuracy and reliability of carbon price predictions.

Main Methods:

  • Optimal feature reconstruction using variational mode decomposition (VMD) and sample entropy (SE).
  • Biphasic deep learning integrating deep recurrent neural network (DRNN) for component prediction.
  • Gate recurrent unit (GRU) for nonlinear integration and final price prediction.
  • Empirical validation using carbon price data from Beijing, Hubei, and Shanghai.

Main Results:

  • The proposed hybrid model significantly improves carbon price predictive accuracy.
  • Statistical measurements confirm the enhanced performance of the multiscale nonlinear integration model.
  • The model effectively extracts features and captures nonlinear dynamics.

Conclusions:

  • The novel hybrid model offers an efficient and accurate approach to carbon price forecasting.
  • This method provides a valuable tool for investors and regulators in carbon markets.
  • The findings contribute to better energy conservation and emission reduction planning.