#Comex copper series#

PVC/PVC with Overall Shielded Pairs/Triads.PVC/PVC with Individual and Overall Shielded Triads.PVC/PVC with Individual and Overall Shielded Pairs.FR-EPR/CPE with Overall Shielded Pairs/Triads.FR-EPR/CPE with Individual and Overall Shielded Triads.FR-EPR/CPE with Individual and Overall Shielded Pairs.James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning: with Applications in R. Hamel, L.H.: Knowledge Discovery with Support Vector Machines. Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. Steinwart, I., Christmann, A.: Support Vector Machines, Springer, New York (2008) Bloomberg Engineering and Mining Journal Platts Metals Week and Thomson Reuters Datastream World Bank. World Bank Commodity Price Data (The Pink Sheet).

#Comex copper series#

Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications: With R Examples. Policy 45, 37–43 (2015)īrockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Sánchez Lasheras, F., de Cos Juez, F.J., Suárez Sánchez, A., Krzemień, A., Riesgo Fernández, P.: Forecasting the COMEX copper spot price by means of neural networks and ARIMA models. Kriechbaumer, T., Angus, A., Parsons, D., Rivas Casado, M.: An improved wavelet–ARIMA approach for forecasting metal prices. Ma, W., Zhu, X., Wang, M.: Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm. Khashei, M., Bijari, M.: An artificial neural network (p, d, q) model for timeseries forecasting. 30, 208–217 (2005)Ĭortazar, G., Eterovic, F.: Can oil prices help estimate commodity futures prices? The cases of copper and silver. 103, 1209–1214 (2006)ĭooley, G., Lenihan, H.: An assessment of time series methods in metal price forecasting. Gordon, R.B., Bertram, M., Graedel, T.E.: Metal stocks and sustainability. Tilton, J.E., Lagos, G.: Assessing the long-run availability of copper. Roache, S.K.: China’s impact on world commodity markets (2012) Streifel, S.: Impact of China and India on global commodity markets focus on metals & minerals and petroleum (2006)Ĭuddington, J.T., Jerrett, D.: Super cycles in real metals prices? IMF Staff Pap. The findings of this research work are in line of with some previous studies, which confirmed the superiority of SVR models over other classical techniques in relative research areas. 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. This article examines the forecasting performance of the three kinds of SVR models using published data of copper spot prices from the New York Commodities Exchange (COMEX). Three different strategies (direct multi-step scheme, recursive multi-step scheme and direct-recursive hybrid scheme) for automatic lag selection in time series analysis are proposed.

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.