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Chaotic Time Series for Copper’s Price Forecast

Neural Networks and the Discovery of Knowledge for Big Data
  • Raúl CarrascoEmail author
  • Manuel Vargas
  • Ismael Soto
  • Diego Fuentealba
  • Leonardo Banguera
  • Guillermo Fuertes
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 527)

Abstract

We investigated the potential of Artificial Neural Networks (ANN), ANN to forecasts in chaotic series of the price of copper; based on different combinations of structure and possibilities of knowledge in big discovery data. Two neural network models were built to predict the price of copper of the London Metal Exchange (LME) with lots of 100 to 1000 data. We used the Feed Forward Neural Network (FFNN) algorithm and Cascade Forward Neural Network (CFNN) combining training, transfer and performance implemented functions in MatLab. The main findings support the use of the ANN in financial forecasts in series of copper prices. The copper price’s forecast using different batches size of data can be improved by changing the number of neurons, functions of transfer, and functions of performance s. In addition, a negative correlation of −0.79 was found in performance indicators using RMS and IA.

Keywords

Big Data Copper price Chaos theory Neural network Nonlinear systems Time series forecasting 

Notes

Acknowledgment

The authors are acknowledgment the financing of the project “Multiuser VLC for underground mining”, code: IT17M10012 and this research has been supported by DICYT (Scientific and Technological Research Bureau) of The University of Santiago of Chile (USACH) and Department of Industrial Engineering.

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Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  1. 1.Facultad de Administración y EconomíaUniversidad de Santiago de ChileSantiagoChile
  2. 2.Facultad de Ingeniería, Ciencia y TecnologíaUniversidad Bernardo O’HigginsSantiagoChile
  3. 3.Departamento de Ingeniería IndustrialUniversidad San SebastiánSantiagoChile
  4. 4.Departamento de Ingeniería IndustrialUniversidad de Santiago de ChileSantiagoChile
  5. 5.Departamento de Ingeniería EléctricaUniversidad de Santiago de ChileSantiagoChile
  6. 6.Informatics Research CentreUniversity of ReadingReadingUK
  7. 7.School of Informatics and TelecommunicationsUniversidad Tecnológica de Chile-INACAPSantiagoChile
  8. 8.Department of Industrial EngineeringUniversity of GuayaquilGuayaquilEcuador

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