Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm

doi:10.1016/j.resourpol.2021.102335

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	Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm
	Du, Pei 1; Guo, Ju'e 1; Sun, Shaolong 1; Wang, Shouyang 2; Wu, Jing 1
	2021-12-01
发表期刊	RESOURCES POLICY
ISSN	0301-4207
卷号	74 页码:10
摘要	The prediction of metal prices can provide references of future investment and decision-making for mining projects and affiliates. However, the improvement of metal prices prediction accuracy is still a highly challenging task owing to the complex influencing factors and nonlinear features. Most previous models usually overlook the significances of model optimization and data preconditioning, and often fail to obtain satisfactory results. Therefore, this study designed a hybrid model for multi-step copper and gold prices prediction. First, a robust data processing algorithm was introduced to decompose time series into several modes, which can effectively identify and mine the main characteristics of metal prices time series. Second, to enhance the prediction performance and conquer the limitations of individual models, nature-inspired metaheuristic algorithms are adopted to tune the parameters of these forecasting models. Third, two metal price data sets, hypothesis testing, seven model performance evaluation indices and several experiments are used to comprehensively compare and analyze the prediction ability of the proposed model. Finally, the results revealed that the developed hybrid model can not only significantly outperform the comparison models considered in this study, also offer effective and valuable suggestions for the related policymakers, investors and metal production and trading enterprises in metal market.
关键词	Metal prices forecasting Data processing method Optimized extreme learning machine Hybrid forecasting model
DOI	10.1016/j.resourpol.2021.102335
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[72101197] ; National Natural Science Foundation of China[71988101] ; Fundamental Research Funds for the Central Universities[SK2021007]
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Studies
WOS记录号	WOS:000700368100098
出版者	ELSEVIER SCI LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/59312
专题	中国科学院数学与系统科学研究院
通讯作者	Wu, Jing
作者单位	1.Xi An Jiao Tong Univ, Sch Management, 28 Xianning West Rd, Xian 710049, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Du, Pei,Guo, Ju'e,Sun, Shaolong,et al. Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm[J]. RESOURCES POLICY,2021,74:10.
APA	Du, Pei,Guo, Ju'e,Sun, Shaolong,Wang, Shouyang,&Wu, Jing.(2021).Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm.RESOURCES POLICY,74,10.
MLA	Du, Pei,et al."Multi-step metal prices forecasting based on a data preprocessing method and an optimized extreme learning machine by marine predators algorithm".RESOURCES POLICY 74(2021):10.