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Surrogate Model Development for Digital Experiments in Welding
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Comparative Evaluation of Deep Learning Model Complexity for Forecasting Non-Ferrous Metal Prices.

LiangHong Li1, TaiHua Guan2, LongXuan Li3

  • 1The Institute for Sustainable Development, Macau University of Science and Technology.

Journal of Visualized Experiments : Jove
|June 22, 2026
PubMed
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Simpler deep learning models, like the gated recurrent unit (GRU), outperform complex architectures for commodity price forecasting. Overly complex models with attention or extra layers reduce accuracy, showing that simpler is often better for financial predictions.

Area of Science:

  • Quantitative Finance
  • Machine Learning
  • Econometrics

Background:

  • Accurate commodity price forecasting is crucial for financial markets.
  • Deep learning models offer potential for improved financial time-series prediction.
  • The impact of architectural complexity on deep learning forecasting accuracy remains an active research area.

Purpose of the Study:

  • To investigate whether increased architectural complexity in deep learning models enhances financial forecasting accuracy.
  • To compare the performance of various deep learning architectures against traditional econometric and machine learning models.
  • To evaluate model generalizability across different commodities (copper, aluminum, zinc).

Main Methods:

  • Utilized daily spot price data from the Shanghai Metals Market (Jan 2015 - Sep 2025).

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Last Updated: Jul 1, 2026

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

  • Applied a standardized preprocessing pipeline including z-score normalization and sliding window techniques.
  • Systematically evaluated eighteen models: GRU, LSTM, CNN-BiLSTM-Attention hybrids, ARIMA, GARCH, Random Forest, XGBoost, and Transformer.

    • Main Results:

    • The standard Gated Recurrent Unit (GRU) model demonstrated the lowest error rates (MAE=1032.85) and highest R-squared (0.907) on copper data.
    • GRU models showed strong generalizability on aluminum (MAE=167.51, R-squared=0.918) and zinc (MAE=254.23, R-squared=0.952) datasets.
    • Ablation studies revealed that adding complexity (attention, bidirectional layers, convolutional modules) decreased predictive accuracy, with significant differences confirmed by the Diebold-Mariano test (p < 0.05).

    Conclusions:

    • Unnecessary architectural complexity in deep learning models can hinder financial forecasting performance.
    • Simpler, robust models like the standard GRU are effective for commodity price prediction.
    • Findings advocate for parsimonious model selection in financial time-series analysis.