A Meta-heuristic Approach for Copper Price Forecasting

  • Fabián Seguel
  • Raúl Carrasco
  • Pablo Adasme
  • Miguel Alfaro
  • Ismael Soto
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 449)


The price of copper and its variations represent a very important financial issue for mining companies and for the Chilean government because of its impact on the national economy. The price of commodities such as copper is highly volatile, dynamic and troublous. Due to this, forecasting is very complex. Using publicly data from October 24th of 2013 to August 29th of 2014 a multivaried based model using meta-heuristic optimization techniques is proposed. In particular, we use Genetic Algorithms and Simulated Annealing in order to find the best fitting parameters to forecast the variation on the copper price. A non-parametric test proposed by Timmermann and Pesaran is used to demonstrate the forecasting capacity of the models. Our numerical results show that the Genetic Algorithmic approach has a better performance than Simulated Annealing, being more effective for long range forecasting.


Genetic Algorithms Simulated Annealing Forecasting Simulated Annealing Copper 


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Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Fabián Seguel
    • 1
  • Raúl Carrasco
    • 1
  • Pablo Adasme
    • 1
  • Miguel Alfaro
    • 2
  • Ismael Soto
    • 1
  1. 1.Department of Electrical EngineeringUniversidad de Santiago de ChileEstación CentralChile
  2. 2.Department of Industrial EngineeringUniversidad de Santiago de ChileEstación CentralChile

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