摘要：

工业时序预测对于优化生产过程和增强决策制定至关重要。现有基于深度学习的方法由于缺乏领域知识而常常效果不理想。现有研究使用机理模型指导深度学习以解决此问题，但这些方法通常只考虑单一机理模型，忽略了工业过程中多个时序预测机理的情形以及工业时序的复杂性。为此，提出基于注意力机制的多机理指导的深度学习工业时序预测(M-MDLITF)通用框架，其能够将多个机理嵌入深度工业时序预测网络指导训练，并且将不同机理的优势通过注意力机制集成于最终预测结果。多机理深度维纳(M-DeepWiener)作为M-MDLITF框架的实例化方法，利用上下文滑动窗口和Transformer编码器架构来挖掘工业时序的复杂模式。在1个模拟数据集和2个真实数据集上的实验结果表明，M-DeepWiener具有良好的运行效率和鲁棒性，比单机理深度维纳(DeepWiener)、经典维纳机理和纯数据驱动方法具有更高的预测准确率，其中在模拟数据集上比单机理模型DeepWiener-M1的误差降低了20%。

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

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.

Key words: industrial time-series prediction, deep learning, mechanism model, multi-mechanism integration, complex pattern mining