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Time Series Forecasting of Gold Prices with the Help of Its Decomposition and Multivariate Adaptive Regression Splines

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16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) (SOCO 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1401))

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Abstract

This research presents a methodology for the forecasting of gold prices using as input information the values of this metal in the previous months and the values of others like potash, copper, lead, tin, nickel, aluminum, iron ore, zinc, platinum and silver. The proposed methodology is based on the decomposition of each of the time series in their trend, seasonal and random components and the use of the trend information as independent variables in a multivariate adaptive regression splines model. The performance of the method was tested with the help of a database of the monthly prices of the aforementioned raw materials. The information available starts in January 1960 and goes up to September 2020. The prediction of gold prices from October 2019 to September 2020 showed in the month-by-month prediction model a mean absolute deviation (MAD) of 67.6022, mean square error (MSE) of 9403.1882, root mean square error (RMSE) of 96.9700 and mean absolute percentage error (MAPE) of 3.8803%. In the case of forecasts up to 12 months ahead, the results were a MAD of 293.4832, MSE of 284499.4718, root mean square error of 533.3849 and MAPE of 15.7366%. The results obtained were compared with those given by a multivariate adaptive regression model that made use of the original time series as input data.

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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

    Gregorio Fidalgo Valverde

  3. Department of Risk Assessment and Industrial Safety Plac Gwarków 1, Central Mining Institute, 40-166, Katowice, Poland

    Alicja Krzemień

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

Correspondence to Fernando Sánchez Lasheras .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Hugo Sanjurjo González

  2. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Iker Pastor López

  3. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  5. BISITE Research Group, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Lasheras, F., Nieto, P., García-Gonzalo, E., Valverde, G., Krzemień, A. (2022). Time Series Forecasting of Gold Prices with the Help of Its Decomposition and Multivariate Adaptive Regression Splines. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_13

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