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Volume 12
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Article

Hybrid DE-Optimized GPR and NARX/SVR Models for Forecasting Gold Spot Prices: A Case Study of the Global Commodities Market

by
Esperanza García-Gonzalo
1,
Paulino José García-Nieto
1,*,
Gregorio Fidalgo Valverde
2,
Pedro Riesgo Fernández
2,
Fernando Sánchez Lasheras
1 and
Sergio Luis Suárez Gómez
1
1
Department of Mathematics, Faculty of Sciences, University of Oviedo, 33007 Oviedo, Spain
2
School of Mining, Energy and Materials Engineering, University of Oviedo, 33004 Oviedo, Spain
*
Author to whom correspondence should be addressed.
Mathematics 2024, 12(7), 1039; https://doi.org/10.3390/math12071039
Submission received: 22 February 2024 / Revised: 26 March 2024 / Accepted: 27 March 2024 / Published: 30 March 2024
(This article belongs to the Special Issue Advances in Artificial Intelligence: Models, Optimization, and Machine Learning, 2nd Edition)
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Abstract

In this work, we highlight three different techniques for automatically constructing the dataset for a time-series study: the direct multi-step, the recursive multi-step, and the direct–recursive hybrid scheme. The nonlinear autoregressive with exogenous variable support vector regression (NARX SVR) and the Gaussian process regression (GPR), combined with the differential evolution (DE) for parameter tuning, are the two novel hybrid methods used in this study. The hyper-parameter settings used in the GPR and SVR training processes as part of this optimization technique DE significantly affect how accurate the regression is. The accuracy in the prediction of DE/GPR and DE/SVR, with or without NARX, is examined in this article using data on spot gold prices from the New York Commodities Exchange (COMEX) that have been made publicly available. According to RMSE statistics, the numerical results obtained demonstrate that NARX DE/SVR achieved the best results.
Keywords: Gaussian process regression (GPR); time-series analysis; differential evolution (DE); support vector regression (SVR); New York Commodity Exchange; gold price forecasting Gaussian process regression (GPR); time-series analysis; differential evolution (DE); support vector regression (SVR); New York Commodity Exchange; gold price forecasting

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MDPI and ACS Style

García-Gonzalo, E.; García-Nieto, P.J.; Fidalgo Valverde, G.; Riesgo Fernández, P.; Sánchez Lasheras, F.; Suárez Gómez, S.L. Hybrid DE-Optimized GPR and NARX/SVR Models for Forecasting Gold Spot Prices: A Case Study of the Global Commodities Market. Mathematics 2024, 12, 1039. https://doi.org/10.3390/math12071039

AMA Style

García-Gonzalo E, García-Nieto PJ, Fidalgo Valverde G, Riesgo Fernández P, Sánchez Lasheras F, Suárez Gómez SL. Hybrid DE-Optimized GPR and NARX/SVR Models for Forecasting Gold Spot Prices: A Case Study of the Global Commodities Market. Mathematics. 2024; 12(7):1039. https://doi.org/10.3390/math12071039

Chicago/Turabian Style

García-Gonzalo, Esperanza, Paulino José García-Nieto, Gregorio Fidalgo Valverde, Pedro Riesgo Fernández, Fernando Sánchez Lasheras, and Sergio Luis Suárez Gómez. 2024. "Hybrid DE-Optimized GPR and NARX/SVR Models for Forecasting Gold Spot Prices: A Case Study of the Global Commodities Market" Mathematics 12, no. 7: 1039. https://doi.org/10.3390/math12071039

APA Style

García-Gonzalo, E., García-Nieto, P. J., Fidalgo Valverde, G., Riesgo Fernández, P., Sánchez Lasheras, F., & Suárez Gómez, S. L. (2024). Hybrid DE-Optimized GPR and NARX/SVR Models for Forecasting Gold Spot Prices: A Case Study of the Global Commodities Market. Mathematics, 12(7), 1039. https://doi.org/10.3390/math12071039

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