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<p>Copper is a vital industrial metal that has widespread applications across various industries of the economy, including electrical wiring, construction, and manufacturing. Copper price prediction is extremely challenging with high price volatility, especially using traditional statistical methods. In this study, a hybrid artificial intelligence model that combines Variational Mode Decomposition (VMD), Extreme Learning Machine (ELM), and Barnacles Mating Optimizer (BMO) is proposed to effectively predict copper prices. The model is trained with historical open, high, low, and close (OHLC) price data collected from 2014 to 2023. VMD is used to decompose input data to enhance signal quality, and BMO fine-tunes the parameters of ELM. Performance metrics include R², RMSE, MAPE, RAE, and RSE. The VMD-BMO-ELM model had better prediction accuracy than baseline models, suggesting that it has the potential to serve as a reliable forecasting tool for investors, policymakers, and the metals market.</p>

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Introduce a new hybrid intelligence model that combines the VMD-ELM and BMO optimizer to estimate copper prices

  • 期刊论文

  • 发表日期：

    2025年7月25日

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  • Wei Yu

摘要

Copper is a vital industrial metal that has widespread applications across various industries of the economy, including electrical wiring, construction, and manufacturing. Copper price prediction is extremely challenging with high price volatility, especially using traditional statistical methods. In this study, a hybrid artificial intelligence model that combines Variational Mode Decomposition (VMD), Extreme Learning Machine (ELM), and Barnacles Mating Optimizer (BMO) is proposed to effectively predict copper prices. The model is trained with historical open, high, low, and close (OHLC) price data collected from 2014 to 2023. VMD is used to decompose input data to enhance signal quality, and BMO fine-tunes the parameters of ELM. Performance metrics include R², RMSE, MAPE, RAE, and RSE. The VMD-BMO-ELM model had better prediction accuracy than baseline models, suggesting that it has the potential to serve as a reliable forecasting tool for investors, policymakers, and the metals market.