A Multivariate Approach to Time Series Forecasting of Copper Prices with the Help of Multiple Imputation by Chained Equations and Multivariate Adaptive Regression Splines | SpringerLink
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A Multivariate Approach to Time Series Forecasting of Copper Prices with the Help of Multiple Imputation by Chained Equations and Multivariate Adaptive Regression Splines

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15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020) (SOCO 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1268))

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Abstract

This research presents a novel methodology for the forecasting of copper prices using as input information the values of this non-ferrous material and the prices of other raw materials. The proposed methodology is based on the use of multiple imputation with chained equations (MICE) in order to forecast the values of the missing data and then to train multivariate adaptive regression splines models capable of predicting the price of copper in advance. The performance of the method was tested with the help of a database of the monthly prices of 72 different raw materials, including copper. The information available starts on January 1960. The prediction of prices from September 2018 to August 2019 showed a root mean squared error (RMSE) value of 318.7996, a mean absolute percentage error (MAPE) of 0.0418 and a mean absolute error (MAE) of 252.8567. The main strengths of the proposed algorithm are two-fold. On the one hand, it can be applied in a systematic way and the results are obtained without any human with expert knowledge having to take any decision; on the other hand, all the trained models are MARS. This means that the models are equations that can be read and understood, and not black box models like artificial neural networks.

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Authors and Affiliations

  1. Faculty of Sciences, University of Oviedo, c/ Federico García Lorca 18, 33007, Oviedo, Spain

    Fernando Sánchez Lasheras, Paulino José García Nieto & Esperanza García-Gonzalo

  2. School of Mining, Energy and Materials Engineering, University of Oviedo, c/ Independencia 13, 33004, Oviedo, Spain

    Javier Gracia Rodríguez & Gregorio Fidalgo Valverde

Authors
  1. Fernando Sánchez Lasheras
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  2. Javier Gracia Rodríguez
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  3. Paulino José García Nieto
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  4. Esperanza García-Gonzalo
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  5. Gregorio Fidalgo Valverde
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Corresponding author

Correspondence to Fernando Sánchez Lasheras .

Editor information

Editors and Affiliations

  1. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Álvaro Herrero

  2. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Carlos Cambra

  3. Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain

    Daniel Urda

  4. Technological Institute of Castilla y León, Burgos, Spain

    Javier Sedano

  5. Department of Industrial Engineering, University of A Coruña, La Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Sánchez Lasheras, F., Gracia Rodríguez, J., García Nieto, P.J., García-Gonzalo, E., Fidalgo Valverde, G. (2021). A Multivariate Approach to Time Series Forecasting of Copper Prices with the Help of Multiple Imputation by Chained Equations and Multivariate Adaptive Regression Splines. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_66

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