Copper Price Prediction Using Support Vector Regression Technique
Predicting 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...
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| Language: | English |
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MDPI AG
2020-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/19/6648 |
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| author | Gabriel Astudillo Raúl Carrasco Christian Fernández-Campusano Máx Chacón |
| author_facet | Gabriel Astudillo Raúl Carrasco Christian Fernández-Campusano Máx Chacón |
| author_sort | Gabriel Astudillo |
| collection | DOAJ |
| description | Predicting 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 (<i>C</i>, <inline-formula><math display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula>) 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 <inline-formula><math display="inline"><semantics><mrow><mn>2.2</mn><mo>%</mo></mrow></semantics></math></inline-formula> for prediction periods of 5 and 10 days. |
| first_indexed | 2024-03-10T16:07:02Z |
| format | Article |
| id | doaj.art-c80d955659d344978f24a21ed6be83d1 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T16:07:02Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c80d955659d344978f24a21ed6be83d12023-11-20T14:49:22ZengMDPI AGApplied Sciences2076-34172020-09-011019664810.3390/app10196648Copper Price Prediction Using Support Vector Regression TechniqueGabriel Astudillo0Raúl Carrasco1Christian Fernández-Campusano2Máx Chacón3Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, ChileFacultad de Ingeniería, Ciencia y Tecnología, Universidad Bernardo O’Higgins, Santiago 8370993, ChileDepartamento de Ingenierías Multidisciplinares, Universidad de Santiago de Chile, Santiago 9170124, ChileDepartamento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago 9170124, ChilePredicting 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 (<i>C</i>, <inline-formula><math display="inline"><semantics><mi>ε</mi></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mi>γ</mi></semantics></math></inline-formula>) 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 <inline-formula><math display="inline"><semantics><mrow><mn>2.2</mn><mo>%</mo></mrow></semantics></math></inline-formula> for prediction periods of 5 and 10 days.https://www.mdpi.com/2076-3417/10/19/6648copper pricepredictionsupport vector regression |
| spellingShingle | Gabriel Astudillo Raúl Carrasco Christian Fernández-Campusano Máx Chacón Copper Price Prediction Using Support Vector Regression Technique Applied Sciences copper price prediction support vector regression |
| title | Copper Price Prediction Using Support Vector Regression Technique |
| title_full | Copper Price Prediction Using Support Vector Regression Technique |
| title_fullStr | Copper Price Prediction Using Support Vector Regression Technique |
| title_full_unstemmed | Copper Price Prediction Using Support Vector Regression Technique |
| title_short | Copper Price Prediction Using Support Vector Regression Technique |
| title_sort | copper price prediction using support vector regression technique |
| topic | copper price prediction support vector regression |
| url | https://www.mdpi.com/2076-3417/10/19/6648 |
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