This paper focuses on the multivariate time series imputation problem using deep neural architectures. The ubiquitous issue of missing data in both scientific and engineering tasks necessitates the development of an effective and general imputation model. Leveraging the wisdom and expertise garnered from low-rank imputation methods, we power the canonical Transformers with three key knowledge-driven enhancements, including projected temporal attention, global adaptive graph convolution, and Fourier imputation loss. These task-agnostic inductive biases exploit the inherent structures of incomplete time series, and thus make our model versatile for a variety of imputation problems. We demonstrate its superiority in terms of accuracy, efficiency, and flexibility on heterogeneous datasets, including traffic speed, traffic volume, solar energy, smart metering, and air quality. Comprehensive case studies are performed to further strengthen the interpretability. Promising empirical results provide strong conviction that incorporating time series primitives, such as low-rank properties, can substantially facilitate the development of a generalizable model to approach a wide range of spatiotemporal imputation problems.

翻译：本文聚焦于利用深度神经架构解决多变量时间序列插补问题。科学与工程任务中普遍存在的数据缺失难题，要求我们开发高效且通用的插补模型。借鉴低秩插补方法的知识与经验，我们通过三项关键知识驱动增强模块——包括投影时序注意力、全局自适应图卷积及傅里叶插补损失——对经典Transformer进行赋能。这些与任务无关的归纳偏置充分利用了非完整时间序列的内在结构，从而使模型能够灵活应对各类插补问题。在异构数据集（涵盖交通速度、交通流量、太阳能、智能计量及空气质量）上，我们验证了模型在精度、效率与灵活性方面的优越性。通过综合案例研究进一步强化了模型的可解释性。令人鼓舞的实验结果有力证明：融入时间序列基元（如低秩性质）可显著推动通用模型的发展，以应对广泛的时空插补问题。