Abstract
The use of genetic algorithms and techniques of big data support the decision-making of manner effective in problems, such as the variation in copper prices. Today, the price of copper and its variations represent a significant financial issue for mining companies and the Chilean government because of its high impact on the national economy. This paper reviews the forecast of volatility for the copper market over a period, which is of interest to different participants such as producers, consumers, governments and investors. To do this, we propose to apply genetic algorithms to predict the variation in copper prices, in order to improve the degree of certainty by incorporating of the inverse of the percentage of sign prediction PSP.
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The authors are grateful for the financial support of the projects Fondef/Conicyt IT17M10012 and STIC-AmSud 19-STIC-08.
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Carrasco, R. et al. (2020). Copper Price Variation Forecasts Using Genetic Algorithms. In: Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds) Applied Technologies. ICAT 2019. Communications in Computer and Information Science, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-42520-3_23
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