AI 中文总结
针对多变量时间序列插补中全局依赖适应性差、局部依赖不可靠的问题，提出GLAIM框架，含稳定全局依赖构造器和样本条件依赖细化器，在9个真实数据集上实现最优插补性能。
AI 中文摘要
多变量时间序列插补是下游分析的基础，但利用不完整观测值对变量间依赖关系进行建模仍具挑战性。现有方法学习跨样本的全局依赖或每个样本的动态局部依赖：全局依赖稳定，但对样本变化和时间非平稳性的适应性差；局部依赖具有自适应性，但观测值不足时不可靠，会导致错误的信息传播。为解决这些局限，我们提出GLAIM，这是一个面向多变量时间序列插补的全局-局部自适应变量间依赖关系建模框架。GLAIM包含两个互补组件：稳定全局依赖构造器从互补时间表示中推导鲁棒的全局变量间依赖，提供受样本特定缺失和噪声影响较小的稳定骨干；样本条件依赖细化器利用每个样本的时间状态和可用观测值，将该骨干适配到每个样本和时间步，使不完整观测下的局部细化更可靠。在9个真实世界数据集上的大量实验表明，GLAIM在随机缺失和块缺失下达到了最先进性能，对缺失率变化保持鲁棒性，且得益于其互补的全局和局部组件。代码可在该https URL获取。
英文摘要
Multivariate time series imputation is fundamental to downstream analysis, yet modeling inter-variable dependencies with incomplete observations remains challenging. Existing methods learn global dependencies across samples or dynamic local dependencies per sample. Global dependencies are stable but adapt poorly to sample variations and temporal non-stationarity, whereas local dependencies are adaptive yet unreliable when observations are insufficient, causing erroneous information propagation. To address these limitations, we propose GLAIM, a Global-Local Adaptive Inter-variable Dependency Modeling framework for multivariate time series imputation. GLAIM comprises two complementary components. The Stable Global Dependency Constructor derives robust global inter-variable dependencies from complementary temporal representations, providing a stable backbone less affected by sample-specific missingness and noise. The Sample-Conditioned Dependency Refiner adapts this backbone to each sample and time step using its temporal state and available observations, enabling reliable local refinement under incomplete observations. Extensive experiments on nine real-world datasets demonstrate that GLAIM achieves state-of-the-art performance under random and block missingness, remains robust to missing-rate shifts, and benefits from its complementary global and local components. Code is available at https://github.com/LuRenjias/GLAIM.
