Machine Learning-Based Scrap Steel Price Forecasting for the Northeast Chinese Market

Bingzi Jin · Xiaojie Xu

71 citations0/0 files storedInternational Journal of Empirical Economics

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

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

Abstract

Throughout history, governments and investors have relied on predictions of prices for a broad spectrum of commodities. Using time-series data covering 08/23/2013–04/15/2021, this study investigates the challenging problem of predicting scrap steel prices, which are issued daily for the northeast China market. Previous research has not sufficiently taken into account estimates for this significant commodity price measurement. In this instance, Gaussian process regression methods are created using Bayesian optimisation approaches and cross-validation processes, and the resulting price forecasts are constructed. This empirical prediction methodology provides reasonably accurate price estimates for the out-of-sample period from 09/17/2019 to 04/15/2021, with a root mean square error of 9.6951, mean absolute error of 5.4218, and correlation coefficient of 99.9122%. Governments and investors can arrive at informed decisions regarding regional scrap steel markets by using pricing research models.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 71 citations exist upstream.

References

1996A test for independence based on the correlation dimension 2020Corn Cash Price Forecasting 2021Corn cash price forecasting with neural networks 2024Forecasting wholesale prices of yellow corn through the Gaussian process regression 2019Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network 2024Wholesale price forecasts of green grams using the neural network 2024Price forecasting through neural networks for crude oil, heating oil, and natural gas 2024Regional steel price index predictions for North China through machine learning

Cited by

2025Forecasts of wholesale food price indices through Gaussian process regressions 2025Machine learning gold price forecasting 2025Bayesian-Optimized Gaussian Process Calibration for Forecasting Carbon Allowance Prices in Tianjin 2025Machine learning platinum price predictions 2025Predicting Scrap Steel Prices Through Machine Learning for South China

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.