Astudillo, G.; Carrasco, R.; Fernández-Campusano, C.; Chacón, M. Copper Price Prediction Using Support Vector Regression Technique. Appl. Sci.2020, 10, 6648.
Astudillo, G.; Carrasco, R.; Fernández-Campusano, C.; Chacón, M. Copper Price Prediction Using Support Vector Regression Technique. Appl. Sci. 2020, 10, 6648.
Astudillo, G.; Carrasco, R.; Fernández-Campusano, C.; Chacón, M. Copper Price Prediction Using Support Vector Regression Technique. Appl. Sci.2020, 10, 6648.
Astudillo, G.; Carrasco, R.; Fernández-Campusano, C.; Chacón, M. Copper Price Prediction Using Support Vector Regression Technique. Appl. Sci. 2020, 10, 6648.
Abstract
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 non-linear, non-stationary, and which have 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 trend-cycle. In this paper, we study a copper price prediction method using Support Vector Regression. This work explores the potential of the Support Vector Regression 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 Exchanges. 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, being the RMSE equal or less than the 2.2% for prediction periods of 5 and 10 days.
Keywords
copper price; prediction; support vector regression
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
