Scrap steel price predictions for southwest China via machine learning

Bingzi Jin · Xiaojie Xu

26 citations0/0 files storedInnovation and Emerging Technologies

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Forecasting Techniques and Applications Market Dynamics and Volatility Stock Market Forecasting Methods

Abstract

Forecasts of prices for a wide range of commodities have been a source of confidence for governments and investors throughout history. This study examines the difficult task of forecasting scrap steel prices, which are released every day for the southwest China market, leveraging time-series data spanning August 23, 2013 to April 15, 2021. Estimates have not been fully considered in previous studies for this important commodity price assessment. In this case, cross-validation procedures and Bayesian optimization techniques are used to develop Gaussian process regression strategies, and consequent price projections are built. Arriving at a relative root mean square error of 0.4691%, this empirical prediction approach yields fairly precise price projections throughout the out-of-sample stage spanning September 17, 2019 to April 15, 2021. Through the use of price research models, governments and investors may make well-informed judgments on regional markets of scrap steel.

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References

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2025Forecasting Scrap Steel Prices via Machine Learning for the Central Chinese Market

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