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Price prediction of polyester yarn based on multiple linear regression model

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  • Wenyi Qiu
  • Qingjun Mao
  • Chen Liu

Abstract

China’s polyester textile industry is one of the notable contributors to national economy. This paper takes polyester yarn, core raw material in polyester textile industry chain, as research object, and deeply explores its price indicators and risk hedging mechanisms through multiple linear regression models and Holt-Winters approaches. It is worth mentioning that with continuous development of digital technology, digital transformation of production lines and warehouses has become an important development feature in various industries. This study also actively complies with this trend, and innovatively incorporates the upstream and downstream production line start-up rates into price prediction model. Through this initiative, we can more comprehensively consider the impact of supply and demand changes on price of polyester yarn, thus making prediction results more closely reflect the actual market situation. This quantitative analysis method undoubtedly provides new ideas for enterprises to better grasp market dynamics in digital era.

Suggested Citation

  • Wenyi Qiu & Qingjun Mao & Chen Liu, 2024. "Price prediction of polyester yarn based on multiple linear regression model," PLOS ONE, Public Library of Science, vol. 19(9), pages 1-16, September.
    • Handle: RePEc:plo:pone00:0310355
    DOI: 10.1371/journal.pone.0310355
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    References listed on IDEAS

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    1. Qi Zhang & Yi Hu & Jianbin Jiao & Shouyang Wang, 2022. "Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
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