Copper Price Prediction Using Support Vector Regression Technique

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dc.contributor.authorAstudillo, Gabriel
dc.contributor.authorCarrasco, Raúl
dc.contributor.authorFernández-Campusano, Christian
dc.contributor.authorChacón, Máx
dc.date.accessioned2022-03-17T20:12:02Z
dc.date.available2022-03-17T20:12:02Z
dc.date.issued2020
dc.description.abstractPredicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is nonlinear and non-stationary, and that has periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes, the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of a trend-cycle. In this paper, we study a copper price prediction method using support vector regression (SVR). This work explores the potential of the SVR with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchange. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data sets, our results obtained indicate that the parameters (C, ε, γ) of the model support vector regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, as the root-mean-square error (RMSE) was equal to or less than the 2.2% for prediction periods of 5 and 10 days.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.identifier.doi10.3390/app10196648
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/3877
dc.language.isoenen_ES
dc.publisherMDPIen_ES
dc.sourceApplied Scienceen_ES
dc.subjectCOPPER PRICEen_ES
dc.subjectPREDICTIONen_ES
dc.subjectSUPPORT VECTOR REGRESSIONen_ES
dc.titleCopper Price Prediction Using Support Vector Regression Techniqueen_ES
dc.typeArticuloen_ES
uv.departamentoEscuela de Ingenieria Informaticaen_ES

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