Copper Price Time Series Forecasting by Means of Generalized Regression Neural Networks with Optimized Predictor Variables

  1. Gregorio Fidalgo Valverde 1
  2. Alicja Krzemień 2
  3. Pedro Riesgo Fernández 1
  4. Francisco Javier Iglesias Rodríguez 1
  5. Ana Suárez Sánchez 1
  1. 1 Universidad de Oviedo
    info

    Universidad de Oviedo

    Oviedo, España

    ROR https://ror.org/006gksa02

  2. 2 Central Mining Institute
    info

    Central Mining Institute

    Katowice, Polonia

    ROR https://ror.org/0367ap631

Libro:
15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020
  1. Álvaro Herrero (coord.)
  2. Carlos Cambra (coord.)
  3. Daniel Urda (coord.)
  4. Javier Sedano (coord.)
  5. Héctor Quintián (coord.)
  6. Emilio Corchado (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-57801-5,  978-3-030-57802-2

Año de publicación: 2021

Páginas: 681-690

Congreso: International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO (15. 2020. Burgos)

Tipo: Aportación congreso

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Resumen

This paper presents a twelve-month forecast of copper price time series developed by means of Generalized regression neural networks with optimized predictor variables. To achieve this goal, in first place the optimum size of the lagged variable was estimated by trial and error method. Second, the order in the time series of the lagged variables was considered and introduced in the predictor variable. A combination of metrics using the Root mean squared error, the Mean absolute error as well as the Standard deviation of absolute error, were selected as figures of merit. Training results clearly state that both optimizations allow improving the forecasting performance.

Fuente de los datos: Dialnet