Bibliographic Details
| Title: |
A support vector regression model for time series forecasting of the COMEX copper spot price. |
| Authors: |
García-Gonzalo, Esperanza1 (AUTHOR), Nieto, Paulino José García1 (AUTHOR), Rodríguez, Javier Gracia2 (AUTHOR), Lasheras, Fernando Sánchez1 (AUTHOR), Valverde, Gregorio Fidalgo2 (AUTHOR) |
| Source: |
Logic Journal of the IGPL. Aug2023, Vol. 31 Issue 4, p775-784. 10p. |
| Subject Terms: |
*Spot prices, *Regression analysis, *Forecasting, *Commodity exchanges, Copper prices |
| Geographic Terms: |
New York (State) |
| Abstract: |
The price of copper is unstable but it is considered an important indicator of the global economy. Changes in the price of copper point to higher global growth or an impending recession. In this work, the forecasting of the spot prices of copper from the New York Commodity Exchange is studied using a machine learning method, support vector regression coupled with different model schemas (recursive, direct and hybrid multi-step). Using these techniques, three different time series analyses are built and its performance are compared. The numerical results show that the hybrid direct-recursive obtains the best results. [ABSTRACT FROM AUTHOR] |
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Copyright of Logic Journal of the IGPL is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Business Source Complete |
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