A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory

Guo, H; Wang, J; Li, Z; Lu, H; Zhang, L

A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory

Guo, H Wang, J Li, Z Lu, H

Zhang, L

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Resources Policy, 2022, 79
Issue Date:: 2022-12-01

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Field	Value	Language
dc.contributor.author	Guo, H
dc.contributor.author	Wang, J
dc.contributor.author	Li, Z
dc.contributor.author	Lu, H https://orcid.org/0000-0001-5655-0237
dc.contributor.author	Zhang, L
dc.date.accessioned	2023-02-07T00:53:03Z
dc.date.available	2023-02-07T00:53:03Z
dc.date.issued	2022-12-01
dc.identifier.citation	Resources Policy, 2022, 79
dc.identifier.issn	0301-4207
dc.identifier.issn	1873-7641
dc.identifier.uri	http://hdl.handle.net/10453/165952
dc.description.abstract	Non-ferrous metal futures, as a significant component of the financial market, are complementary and coordinated with other financial elements, which has been a key area of research in recent years. However, given the apparent volatility and chaotic nature of the non-ferrous metal price sequence, forecasting it remains a difficult challenge. While prior research employed a variety of methodologies to forecast metal prices, they overlooked the critical role of chaos feature analysis and the necessity of error analysis, severely limiting prediction accuracy. This paper designs a novel non-ferrous metal price ensemble prediction system that incorporates data decomposition, phase space reconstruction, multi-objective optimization, point prediction, and interval prediction. A combined kernel extreme learning machine based on the improved multi-objective lion swarm optimization algorithm is developed and theoretically explained to improve prediction accuracy and reliability. Additionally, the appropriate creation of the prediction interval based on the best-fit distribution of the point prediction error enabled the examination of various levels of uncertainty. In an empirical experiment using copper and aluminum prices from the London Metal Exchange, the proposed system demonstrated benefits in point and interval prediction, providing decision makers with useful prediction references.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Resources Policy
dc.relation.isbasedon	10.1016/j.resourpol.2022.102975
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0914 Resources Engineering and Extractive Metallurgy, 1605 Policy and Administration
dc.subject.classification	Environmental Sciences
dc.title	A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory
dc.type	Journal Article
utslib.citation.volume	79
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/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	embargoed	*
utslib.copyright.embargo	2024-12-01T00:00:00+1000Z
dc.date.updated	2023-02-07T00:52:22Z
pubs.publication-status	Published
pubs.volume	79

Abstract:

Non-ferrous metal futures, as a significant component of the financial market, are complementary and coordinated with other financial elements, which has been a key area of research in recent years. However, given the apparent volatility and chaotic nature of the non-ferrous metal price sequence, forecasting it remains a difficult challenge. While prior research employed a variety of methodologies to forecast metal prices, they overlooked the critical role of chaos feature analysis and the necessity of error analysis, severely limiting prediction accuracy. This paper designs a novel non-ferrous metal price ensemble prediction system that incorporates data decomposition, phase space reconstruction, multi-objective optimization, point prediction, and interval prediction. A combined kernel extreme learning machine based on the improved multi-objective lion swarm optimization algorithm is developed and theoretically explained to improve prediction accuracy and reliability. Additionally, the appropriate creation of the prediction interval based on the best-fit distribution of the point prediction error enabled the examination of various levels of uncertainty. In an empirical experiment using copper and aluminum prices from the London Metal Exchange, the proposed system demonstrated benefits in point and interval prediction, providing decision makers with useful prediction references.

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http://hdl.handle.net/10453/165952