Open AccessA Meta-heuristic Approach for Copper Price Forecasting
Author(s) -
Fabián Seguel,
Raúl Carrasco,
Pablo Adasme,
Miguel Alfaro,
Ismael Soto
Publication year - 2015
Publication title -
ifip advances in information and communication technology
Language(s) - English
Resource type - Book series
SCImago Journal Rank - 0.189
H-Index - 53
eISSN - 1868-422X
pISSN - 1868-4238
DOI - 10.1007/978-3-319-16274-4_16
Subject(s) - simulated annealing , econometrics , parametric statistics , genetic algorithm , computer science , copper , heuristic , univariate , range (aeronautics) , adaptive simulated annealing , economics , data mining , mathematical optimization , artificial intelligence , machine learning , mathematics , statistics , engineering , multivariate statistics , materials science , metallurgy , aerospace engineering
Part 4: Complex System Modelling and SimulationInternational audienceThe 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
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