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dc.contributor.authorWang, Yanyi
dc.contributor.authorGuo, Zhenwei
dc.contributor.authorZhang, Yunrui
dc.contributor.authorHu, Xiangping
dc.contributor.authorXiao, Jianping
dc.date.accessioned2023-12-06T07:19:11Z
dc.date.available2023-12-06T07:19:11Z
dc.date.created2023-12-05T09:39:03Z
dc.date.issued2023
dc.identifier.citationSustainability. 2023, 15 (22), .en_US
dc.identifier.issn2071-1050
dc.identifier.urihttps://hdl.handle.net/11250/3106128
dc.description.abstractThe fluctuation of iron ore prices is one of the most important factors affecting policy. Therefore, the accurate prediction of iron ore prices has significant value in analysis and judgment regarding future changes in policies. In this study, we propose a correlation analysis to extract eight influencing factors of iron ore prices and introduce multiple linear regression analysis to the prediction. With historical data, we establish a model to forecast iron ore prices from 2020 to 2024. Taking prices in 2018 and 2019 as samples to test the applicability of the model, we obtain an acceptable level of error between the predicted iron ore prices and the actual prices. The prediction model based on multiple linear regression has high prediction accuracy. Iron ore prices will show a relatively stable upward trend over the next five years without the effects of COVID-19.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleIron Ore Price Prediction Based on Multiple Linear Regression Modelen_US
dc.title.alternativeIron Ore Price Prediction Based on Multiple Linear Regression Modelen_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume15en_US
dc.source.journalSustainabilityen_US
dc.source.issue22en_US
dc.identifier.doi10.3390/su152215864
dc.identifier.cristin2208924
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode0


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