计算机工程2025，Vol.51Issue(7)：47-58,12.DOI:10.19678/j.issn.1000-3428.0069406
多机理指导的深度学习工业时序预测框架
Multi-mechanism-guided Deep Learning Framework for Industrial Time-series Forecasting
摘要
Abstract
Industrial time-series forecasting is critical for optimizing production processes and enhancing decision-making.Existing deep learning-based methods often underperform in this context due to a lack of domain knowledge.Prior studies have proposed using mechanistic models to guide deep learning;however,these approaches typically consider only a single mechanistic model,ignoring scenarios with multiple time-series prediction mechanisms in industrial processes and the inherent complexity of industrial time-series(e.g.,multiscale dynamics and nonlinearity).To address this issue,this study proposes a Multi-Mechanism-guided Deep Learning for Industrial Time-series Forecasting(M-MDLITF)framework based on attention mechanisms.This framework embeds multiple mechanistic models into a deep industrial time-series prediction network to guide training and integrate the strengths of different mechanisms by focusing on final predictions.As an instantiation of the M-MDLITF,the Multi-mechanism Deep Wiener(M-DeepWiener)method employs contextual sliding windows and a Transformer-encoder architecture to capture complex patterns in industrial time-series.Experimental results from a simulated dataset and two real-world datasets demonstrate that M-DeepWiener achieves high computational efficiency and robustness.It significantly outperforms the single-mechanism Deep Wiener(DeepWiener),classical Wiener mechanistic models,and purely data-driven methods,reducing the prediction error by 20％compared to DeepWiener-M1 on the simulated dataset.关键词
工业时序预测/深度学习/机理模型/多机理集成/复杂模式挖掘Key words
industrial time-series prediction/deep learning/mechanism model/multi-mechanism integration/complex pattern mining分类
信息技术与安全科学引用本文复制引用
李姜辛,王鹏,汪卫..多机理指导的深度学习工业时序预测框架[J].计算机工程,2025,51(7):47-58,12.基金项目
国家重点研发计划(2020YFB1710001). （2020YFB1710001）
