Multi-step-ahead copper price forecasting using a two-phase architecture based on an improved LSTM with novel input strategy and error correction

Hongyuan Luo, Deyun Wang, Jinhua Cheng and Qiaosheng Wu

Resources Policy, 2022, vol. 79, issue C

Abstract: Accurate copper price forecasting plays a vital role in many aspects of economics. However, the complicated fluctuations of copper price make it a challenging job. This study develops a novel architecture based on long short-term memory model optimized by genetic algorithm (GA-LSTM) and error correction strategy for multi-step-ahead copper price forecasting. The proposed architecture includes the following two sub-phases: (1) initial forecasting of copper price; (2) error correction. In the first phase, to improve the forecast accuracy, a number of recent copper price data and some selected influencing factors are combined to construct a hybrid input of GA-LSTM model. The hybrid input strategy simultaneously considers the evolution rule of historical price data and the causal relationship between the influencing factors and copper price data, which provides more information for GA-LSTM model. One real copper price data series with a time span of 30 years is used for validating the forecasting ability of the proposed architecture and a same length of iron ore price data series is also adopted for testifying the generalization ability and robustness of the proposed architecture. The experiment results illustrate that the proposed architecture outperforms the benchmark models considered in this study.

Keywords: Copper price forecasting; Long short-term memory; Genetic algorithm; Hybrid input strategy; Error correction (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0301420722004056
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:79:y:2022:i:c:s0301420722004056

DOI: 10.1016/j.resourpol.2022.102962

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 ().