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<p>Governments and investors have traditionally placed reliance on estimates of price time series of many different types of commodities. This study uses time series data from 08/23/2013 to 04/15/2021 to investigate the challenging job of predicting prices of scrap steel, which are issued for the South China market on a daily basis. Previous research has not given enough weight to predictions of this crucial commodity price indicator. Here, price forecasts are generated using Gaussian process regression algorithms built using cross-validation processes and Bayesian optimization methods. This empirical forecast framework provides reasonably accurate price estimates over the out-of-sample period of 09/17/2019–04/15/2021, with a relative root mean square error of 0.3287%. Price research models can be employed by governments and investors to make well-informed decisions on regional scrap steel markets.</p>

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Predicting Scrap Steel Prices Through Machine Learning for South China

  • 期刊论文

  • 发表日期：

    2025年1月6日

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  • Bingzi Jin,
  • Xiaojie Xu

摘要

Governments and investors have traditionally placed reliance on estimates of price time series of many different types of commodities. This study uses time series data from 08/23/2013 to 04/15/2021 to investigate the challenging job of predicting prices of scrap steel, which are issued for the South China market on a daily basis. Previous research has not given enough weight to predictions of this crucial commodity price indicator. Here, price forecasts are generated using Gaussian process regression algorithms built using cross-validation processes and Bayesian optimization methods. This empirical forecast framework provides reasonably accurate price estimates over the out-of-sample period of 09/17/2019–04/15/2021, with a relative root mean square error of 0.3287%. Price research models can be employed by governments and investors to make well-informed decisions on regional scrap steel markets.