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Related Experiment Video

Updated: Jun 13, 2025

Artificial Thermal Ageing of Polyester Reinforced and Polyvinyl Chloride Coated Technical Fabric
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Price prediction of polyester yarn based on multiple linear regression model.

Wenyi Qiu1, Qingjun Mao2, Chen Liu3

  • 1School of Global Education & Development, University of Chinese Academy of Social Sciences-University of Stirling, Beijing, China.

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|September 12, 2024
PubMed
Summary

This study analyzes polyester yarn price indicators and risk hedging. It innovatively integrates production line data into prediction models for more accurate market insights in the digital era.

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Area of Science:

  • Textile Industry Economics
  • Supply Chain Management
  • Quantitative Finance

Background:

  • China's polyester textile sector is economically significant.
  • Polyester yarn is a crucial raw material within this industry.
  • Digital transformation is reshaping industrial operations.

Purpose of the Study:

  • To identify key price indicators for polyester yarn.
  • To explore effective risk hedging mechanisms.
  • To enhance price prediction accuracy by incorporating digital transformation trends.

Main Methods:

  • Multiple linear regression models were employed.
  • Holt-Winters time series analysis was utilized.
  • Upstream and downstream production line start-up rates were innovatively integrated into the prediction model.

Main Results:

  • The study identified significant price indicators for polyester yarn.
  • The integration of production line data improved the accuracy of price predictions.
  • The developed model offers a more comprehensive view of supply and demand dynamics.

Conclusions:

  • The quantitative analysis provides valuable insights for enterprises.
  • Improved price forecasting aids in better market dynamics navigation.
  • The approach supports the digital transformation of the textile industry.