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- Title
Hybrid DE-Optimized GPR and NARX/SVR Models for Forecasting Gold Spot Prices: A Case Study of the Global Commodities Market.
- Authors
García-Gonzalo, Esperanza; García-Nieto, Paulino José; Fidalgo Valverde, Gregorio; Riesgo Fernández, Pedro; Sánchez Lasheras, Fernando; Suárez Gómez, Sergio Luis
- 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.
- Subjects
NEW York (State); GOLD sales &; prices; SPOT prices; COMMODITY exchanges; KRIGING; DIFFERENTIAL evolution
- Publication
Mathematics (2227-7390), 2024, Vol 12, Issue 7, p1039
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math12071039