Abstract
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 multi-step 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 of SVR models 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 of SVR models over other classical techniques in relative research areas.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Streifel, S.: Impact of China and India on global commodity markets focus on metals & minerals and petroleum (2006)
Cuddington, J.T., Jerrett, D.: Super cycles in real metals prices? IMF Staff Pap. 55, 541–565 (2008)
Roache, S.K.: China’s impact on world commodity markets (2012)
Lahart, J.: Ahead of the Tape: Dr. Copper (2006)
Tilton, J.E., Lagos, G.: Assessing the long-run availability of copper. Resour. Policy. 32, 19–23 (2007)
Gordon, R.B., Bertram, M., Graedel, T.E.: Metal stocks and sustainability. Proc. Natl. Acad. Sci. 103, 1209–1214 (2006)
Dooley, G., Lenihan, H.: An assessment of time series methods in metal price forecasting. Resour. Policy. 30, 208–217 (2005)
Cortazar, G., Eterovic, F.: Can oil prices help estimate commodity futures prices? The cases of copper and silver. Resour. Policy 35, 283–291 (2010)
Khashei, M., Bijari, M.: An artificial neural network (p, d, q) model for timeseries forecasting. Expert Syst. Appl. 37, 479–489 (2010)
Ma, W., Zhu, X., Wang, M.: Forecasting iron ore import and consumption of China using grey model optimized by particle swarm optimization algorithm. Resour. Policy 38, 613–620 (2013)
Kriechbaumer, T., Angus, A., Parsons, D., Rivas Casado, M.: An improved wavelet–ARIMA approach for forecasting metal prices. Resour. Policy. 39, 32–41 (2014)
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. Resour. Policy 45, 37–43 (2015)
Brockwell, P.J., Davis, R.A.: Introduction to Time Series and Forecasting. Springer, Cham (2016)
Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications: With R Examples. Springer, Cham (2017)
World Bank Commodity Price Data (The Pink Sheet). Bloomberg; Engineering and Mining Journal; Platts Metals Week; and Thomson Reuters Datastream; World Bank. http://pubdocs.worldbank.org/en/561011486076393416/CMO-Historical-Data-Monthly.xlsx
Steinwart, I., Christmann, A.: Support Vector Machines, Springer, New York (2008)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, Cambridge (2001)
Hamel, L.H.: Knowledge Discovery with Support Vector Machines. Wiley-Interscience (2011)
James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning: with Applications in R. Springer, New York (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
García-Gonzalo, E., García Nieto, P.J., Gracia Rodríguez, J., Sánchez Lasheras, F., Fidalgo Valverde, G. (2021). Time Series Analysis for the COMEX Copper Spot Price by Using Support Vector Regression. In: Herrero, Á., Cambra, C., Urda, D., Sedano, J., Quintián, H., Corchado, E. (eds) 15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020). SOCO 2020. Advances in Intelligent Systems and Computing, vol 1268. Springer, Cham. https://doi.org/10.1007/978-3-030-57802-2_67
Download citation
DOI: https://doi.org/10.1007/978-3-030-57802-2_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57801-5
Online ISBN: 978-3-030-57802-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)