Industrial time-series, as a structural data responds to production process information, can be utilized to perform data-driven decision-making for effective monitoring of industrial production process. However, there are some challenges for time-series forecasting in industry, e.g., predicting few-shot caused by data shortage, and decision-confusing caused by unknown treatment policy. To cope with the problems, we propose a novel causal domain adaptation framework, Causal Domain Adaptation (CDA) forecaster to improve the performance on the interested domain with limited data (target). Firstly, we analyze the causality existing along with treatments, and thus ensure the shared causality over time. Subsequently, we propose an answer-based attention mechanism to achieve domain-invariant representation by the shared causality in both domains. Then, a novel domain-adaptation is built to model treatments and outcomes jointly training on source and target domain. The main insights are that our designed answer-based attention mechanism allows the target domain to leverage the existed causality in source time-series even with different treatments, and our forecaster can predict the counterfactual outcome of industrial time-series, meaning a guidance in production process. Compared with commonly baselines, our method on real-world and synthetic oilfield datasets demonstrates the effectiveness in across-domain prediction and the practicality in guiding production process

翻译：工业时序数据作为反映生产过程信息的结构化数据，可用于执行数据驱动的决策，从而有效监控工业生产过程。然而，工业时序预测面临诸多挑战，例如因数据短缺导致的少样本预测问题，以及未知处理策略引发的决策混淆问题。为应对这些挑战，我们提出一种新颖的因果领域自适应框架——因果领域自适应（CDA）预测器，旨在提升数据有限的目标领域上的预测性能。首先，我们分析伴随处理策略存在的因果关系，从而确保随时间推移的因果共享性。随后，我们提出一种基于答案的注意力机制，通过两个领域共享的因果关系实现领域不变表征。进而构建新型领域自适应模型，在源领域和目标领域上联合训练处理策略与结果变量。本方法的核心洞见在于：设计的基于答案的注意力机制使目标领域能够利用源时序数据中既存的因果关系（即使处理策略不同），且我们的预测器能够预测工业时序数据的反事实结果，从而为生产过程提供决策指导。通过在真实油田数据集与合成数据集上与常用基线方法的对比实验，验证了本方法在跨领域预测中的有效性及其指导生产过程的实用价值。