A novel mixed-frequency deep learning forecasting model for natural resource prices: A case study of copper futures price

Abstract

Accurate prediction of natural resource prices plays a significant role in national economic development. However, existing research often focuses solely on same-frequency forecasting, neglecting the rich information contained in high-frequency data. To bridge this gap and explore whether mixed-frequency prediction improves the forecasting performance, this study develops an innovative mixed-frequency deep learning forecasting model grounded in Pearson correlation coefficient analysis, long-short-term memory, particle swarm optimization, and mixed-frequency data sampling regression. Taking copper price as an example, this study first applies Pearson correlation analysis to select the most relevant influencing factors from mixed-frequency variables. These factors include policy uncertainty, macroeconomic conditions, energy costs, and other non-ferrous metal prices. Subsequently, the proposed mixed-frequency deep learning model is used for predicting copper price. Experiments include comparisons with the benchmark model, multi-step prediction, statistical hypothesis testing, in-depth evaluation of forecasting effectiveness, and robustness analysis. The final experimental results demonstrate that the proposed mixed-frequency deep learning model significantly outperforms the comparison models, effectively improving prediction accuracy. This study not only expands the scope of futures price prediction research, but also provides a new perspective for time series prediction work in other fields.

Keywords

Copper futures price forecasting / Mixed-frequency data sampling regression / Long-short-term memory / Particle swarm optimization

CRediT authorship contribution statement

Pei Du: Writing - review & editing, Writing - original draft, Software, Resources, Methodology, Conceptualization. Mingyang Ji: Writing - original draft, Visualization, Software, Investigation, Data curation. Juntao Du: Writing - review & editing, Writing - original draft, Validation, Supervision, Funding acquisition. Jianzhou Wang: Writing - review & editing, Supervision, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This study was funded by the Humanities and Social Science Fund of the Ministry of Education of China (No. 22YJCZH028), the National Natural Science Foundation of China (Grant No. 72303001), Anhui Provincial Excellent Young Scientists Fund for Universities (No. 2024AH030001) and Anhui Education Department Excellent Young Teachers Fund (No. YQYB2024021).

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