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In this instance, Gaussian process regression algorithms are developed using cross-validation processes and Bayesian optimization approaches, leading to the construction of price forecasts. Our empirical prediction technique produces reasonably accurate price estimates for the out-of-sample period encompassing 17 September 2019\u201315 April 2021, with a relative root mean square error of 0.1053%. Governments and investors may utilize price prediction models to make educated decisions about the scrap steel industry. <\/jats:p>","DOI":"10.1142\/s1752890925500072","type":"journal-article","created":{"date-parts":[[2025,2,7]],"date-time":"2025-02-07T15:10:53Z","timestamp":1738941053000},"source":"Crossref","is-referenced-by-count":23,"title":["National Scrap Steel Price Forecasts Using Gaussian Process Regression Models"],"prefix":"10.1142","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1620-7772","authenticated-orcid":false,"given":"Bingzi","family":"Jin","sequence":"first","affiliation":[{"name":"Advanced Micro Devices (China) Co., Ltd., Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4452-1540","authenticated-orcid":false,"given":"Xiaojie","family":"Xu","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, NC 27695, USA"}]}],"member":"219","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"S1752890925500072BIB001","doi-asserted-by":"publisher","DOI":"10.1142\/S1752890924400026"},{"key":"S1752890925500072BIB002","doi-asserted-by":"publisher","DOI":"10.1039\/D1NJ01523K"},{"key":"S1752890925500072BIB003","doi-asserted-by":"publisher","DOI":"10.1108\/AJEB-06-2024-0070"},{"key":"S1752890925500072BIB004","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-07218-1"},{"key":"S1752890925500072BIB005","doi-asserted-by":"publisher","DOI":"10.1142\/S1752890923500071"},{"issue":"3","key":"S1752890925500072BIB006","first-page":"196","volume":"15","author":"Tang B.-q.","year":"2019","journal-title":"Archit. 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