论文标题
多元时间序列的注意机制复发模型可解释性适用于铁生产行业
Attention Mechanism for Multivariate Time Series Recurrent Model Interpretability Applied to the Ironmaking Industry
论文作者
论文摘要
数据驱动的模型可解释性是获得过程工程师接受以依靠数据驱动模型来规范铁工业中的工业过程的一种要求。在本文介绍的研究中，我们着重于开发可解释的多元时间序列，以预测爆炸炉产生的热金属温度的深度学习体系结构。提出了一个长期的短期记忆（LSTM）结构，并提出了通过注意机制和引导反向传播的，以适应每个输入的局部时间解释性的预测。结果显示，适用于爆炸炉数据的这种体系结构的潜力很高，并提供了正确反映由固有的爆炸炉过程决定的真实复杂变量关系，并且与基于经常性的深度学习体系结构相比，预测错误减少了。
Data-driven model interpretability is a requirement to gain the acceptance of process engineers to rely on the prediction of a data-driven model to regulate industrial processes in the ironmaking industry. In the research presented in this paper, we focus on the development of an interpretable multivariate time series forecasting deep learning architecture for the temperature of the hot metal produced by a blast furnace. A Long Short-Term Memory (LSTM) based architecture enhanced with attention mechanism and guided backpropagation is proposed to accommodate the prediction with a local temporal interpretability for each input. Results are showing high potential for this architecture applied to blast furnace data and providing interpretability correctly reflecting the true complex variables relations dictated by the inherent blast furnace process, and with reduced prediction error compared to a recurrent-based deep learning architecture.
