Time Series Analysis for the COMEX Copper Spot Price by Using Support Vector Regression
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Esperanza García-Gonzalo [1] ; Paulino José García Nieto [1] ; Javier Gracia Rodríguez [1] ; Fernando Sánchez Lasheras [1] ; Gregorio Fidalgo Valverde [1]
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[1] Universidad de Oviedo
Universidad de Oviedo
Oviedo, España
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- Localización: 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020): Burgos, Spain ; September 2020 / coord. por Álvaro Herrero Cosío, Carlos Cambra Baseca, Daniel Urda Muñoz, Javier Sedano Franco, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2021, ISBN 978-3-030-57802-2, págs. 702-708
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
In this research work, support vector regression (SVR), a powerful machine learning tool that can identify nonlinear patterns effectively thanks to the introduction of a kernel function is used. Three different strategies (direct multistep scheme, recursive multi-step scheme and direct-recursive hybrid scheme) for automatic lag selection in time series analysis are proposed. This article examines the forecasting performance of the three kinds ofSVRmodels using published data of copper spot prices from the New York Commodities Exchange (COMEX). The numerical results obtained have shown a better performance of the direct-recursive hybrid scheme than the recursive multi-step scheme and direct multi-step scheme. The findings of this research work are in line of with some previous studies, which confirmed the superiority ofSVRmodels over other classical techniques in relative research areas.
