Abstract: 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.

Key words: process manufacturing, quality prediction, seasonal and trend decomposition, TimesNet, Informer, crested porcupine optimizer (CPO)