A support vector regression model for time series forecasting of the COMEX copper spot price

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.

Automated summary

In this study, a machine learning method is used to forecast the spot prices of copper from the New York Commodity Exchange, and the performance of different model schemas is compared. The numerical results demonstrate that the hybrid direct-recursive method achieves the best results.