Forecasting monthly copper price: A comparative study of various machine learning-based methods

Zhang, H; Nguyen, H; Vu, DA; Bui, XN; Pradhan, B

Forecasting monthly copper price: A comparative study of various machine learning-based methods

Zhang, H Nguyen, H Vu, DA Bui, XN Pradhan, B

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Resources Policy, 2021, 73, pp. 1-19
Issue Date:: 2021-10-01

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Field	Value	Language
dc.contributor.author	Zhang, H
dc.contributor.author	Nguyen, H
dc.contributor.author	Vu, DA
dc.contributor.author	Bui, XN
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.date.accessioned	2022-02-21T01:06:28Z
dc.date.available	2022-02-21T01:06:28Z
dc.date.issued	2021-10-01
dc.identifier.citation	Resources Policy, 2021, 73, pp. 1-19
dc.identifier.issn	0301-4207
dc.identifier.issn	1873-7641
dc.identifier.uri	http://hdl.handle.net/10453/154709
dc.description.abstract	Copper is one of the valuable natural resources, and it was widely used in many different industries. The complicated fluctuations of copper prices can significantly affect other industries. Therefore, this study aims to develop and propose several forecast models for forecasting monthly copper prices in the future based on various algorithms in machine learning, including multi-layer perceptron (MLP) neural network, k-nearest neighbors (KNN), support vector machine (SVM), gradient boosting tree (GBT), and random forest (RF). The monthly copper price dataset from January 1990 to December 2019 was collected for this aim based on other metals and natural gas prices. In addition, the influence of currency exchange rates of the countries that have the largest copper production around the world was also taken into account and used as input variables for forecasting copper price. The different set of predictors (t, t-1, t-2, t-3, t-4. t-5) were considered to forecast monthly copper prices based on the mentioned machine learning techniques. The results revealed that the currency exchange rates of the countries that have the most abundant copper production around the world have a significant effect on the volatility of monthly copper prices in the world, and they should be used to forecast monthly copper prices in the future. A comprehensive comparison of various machine learning techniques shows that MLP neural network (with deep learning techniques) is the best method for forecasting monthly copper price with an MAE of 228.617 and RMSE of 287.539. Whereas, the other models, such as SVM, RF, KNN, and GBT, provided higher errors with an MAE in the range of 308.691–453.147, RMSE in the range of 393.599–552.208. In this sense, MLP neural network can be used as a reliable tool to forecast copper prices in the future.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Resources Policy
dc.relation.isbasedon	10.1016/j.resourpol.2021.102189
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0914 Resources Engineering and Extractive Metallurgy, 1605 Policy and Administration
dc.subject.classification	Environmental Sciences
dc.title	Forecasting monthly copper price: A comparative study of various machine learning-based methods
dc.type	Journal Article
utslib.citation.volume	73
utslib.for	0914 Resources Engineering and Extractive Metallurgy
utslib.for	1605 Policy and Administration
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-02-21T01:06:19Z
pubs.publication-status	Published
pubs.volume	73

Abstract:

Copper is one of the valuable natural resources, and it was widely used in many different industries. The complicated fluctuations of copper prices can significantly affect other industries. Therefore, this study aims to develop and propose several forecast models for forecasting monthly copper prices in the future based on various algorithms in machine learning, including multi-layer perceptron (MLP) neural network, k-nearest neighbors (KNN), support vector machine (SVM), gradient boosting tree (GBT), and random forest (RF). The monthly copper price dataset from January 1990 to December 2019 was collected for this aim based on other metals and natural gas prices. In addition, the influence of currency exchange rates of the countries that have the largest copper production around the world was also taken into account and used as input variables for forecasting copper price. The different set of predictors (t, t-1, t-2, t-3, t-4. t-5) were considered to forecast monthly copper prices based on the mentioned machine learning techniques. The results revealed that the currency exchange rates of the countries that have the most abundant copper production around the world have a significant effect on the volatility of monthly copper prices in the world, and they should be used to forecast monthly copper prices in the future. A comprehensive comparison of various machine learning techniques shows that MLP neural network (with deep learning techniques) is the best method for forecasting monthly copper price with an MAE of 228.617 and RMSE of 287.539. Whereas, the other models, such as SVM, RF, KNN, and GBT, provided higher errors with an MAE in the range of 308.691–453.147, RMSE in the range of 393.599–552.208. In this sense, MLP neural network can be used as a reliable tool to forecast copper prices in the future.

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