Forecasting copper price by application of robust artificial intelligence techniques
Hasel Amini Khoshalan,
Iraj Najmoddini and
Resources Policy, 2021, vol. 73, issue C
Metal price is one of the most important and effective parameters in assessing different projects such as industry and mining. In this regard, price variations can play a vital role in the correct decision-making of managers to develop or limit mining activities. Considering the increasing use of artificial intelligence (AI)-based networks in different fields such as price estimation, four methods were used in the present work for the first time to predict the price of important and extensively used copper-grade A cathode. These methods include Gene expression programming (GEP), Artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS), and ANFIS-ACO (ant colony optimization algorithm). In this process, coal, aluminum, crude oil, gold, iron ore, natural gas, nickel, and lead were selected as the copper price parameters from 1990 to 2020. In this study, the ANN model with one hidden layer comprising 13 neurons, RMSE of 356.51, MAE of 239.105 ($/ton), MAPEof 5.70% ($/ton), and coefficient of determination (R2) of 98.1% for network test data was selected as the best model in predicting copper prices. In terms of their performance, ANFIS, ANFIS - ACO and GEP models were ranked next in the order of their appearance. Overall, an acceptable performance was found through all four AI methods in this study for predicting copper prices.
Keywords: Copper price prediction; GEP; ANN; ANFIS; ANFIS-ACO (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:73:y:2021:i:c:s0301420721002506
Access Statistics for this article
Resources Policy is currently edited by R. G. Eggert
More articles in Resources Policy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().