流程生产工艺数据易受到高噪声干扰，且具有复杂的非线性动态特性和多尺度时序依赖性，使质量的精准预测面临巨大挑战。因此，本文提出了一种融合基于局部加权回归（locally estimated scatterplot smoothing,LOESS）季节趋势分解(seasonal and trend decomposition using LOESS,STL)与冠豪猪优化（crested porcupine optimizer,CPO）算法的并行TimesNet-Informer深度学习预测模型。首先，采用STL方法将原始时间序列分解为趋势项、季节项及残差项，有效提取多尺度时序特征；其次，构建并行结构，融合TimesNet对周期性和局部特征的捕捉能力与Informer在长序列依赖特征提取上的优势，实现复杂工艺过程质量的精准拟合与预测；进一步引入CPO算法对分解参数及模型超参数进行自动优化。以某流程工业生产线的真实数据为例进行实验验证，结果表明，该模型在多项评价指标上均优于其他对比方法，预测精度达到0.979 8，为流程生产工艺质量的精准预测提供了有效的解决方案。
In process manufacturing, quality prediction is particularly challenging due to high levels of noise, complex nonlinear dynamics, and multiscale temporal dependencies in process data. To address these issues, this paper proposes a parallel TimesNet-Informer deep learning prediction model that integrates seasonal-trend decomposition using loess (STL) with the crested porcupine optimizer (CPO). First, the STL method is employed to decompose the original time series into trend, seasonal, and residual components, thereby extracting multiscale temporal features. Second, the CPO algorithm is utilized to auto-matically optimize decomposition parameters and model hyperparameters in a data-driven manner. A parallel architecture is designed that combines the strength of TimesNet in capturing periodic and local features with Informer’s superior capability in modeling long-sequence dependencies, enabling accurate fitting and prediction of complex process quality. The model is validated on real-world data from a process manufacturing production line. Experimental results demonstrate that the proposed model outperforms other comparative models across all evaluation metrics, achieving a prediction accuracy (R2) of 0.979 8. This provides an effective solution for accurate quality prediction in process manufacturing.
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