GLAIM: Learning Global and Local Adaptive Inter-Variable Dependency for Multivariate Time Series Imputation

AI 中文解读

这套AI填补数据缺口的本事又升级了！新模型GLAIM号称“既稳又活”，解决了过去智能填补时“顾此失彼”的难题。简单说，以前AI填补传感器或金融数据的缺失值，要么只会套用固定的老经验，碰上突发情况反应迟钝；要么太“见风使舵”，数据缺得多了，就容易瞎猜导致错误连环传。GLAIM的妙招是“两条腿走路”：先用整体数据摸出一套靠谱的通用规律当压舱石，再根据每个样本的具体情况对这套规律做精细微调。就像老师先教通用解题模板，再针对每个学生的错题单独辅导，既不容易跑偏，又能随机应变。在9个真实数据集上，不管数据是随机丢还是整段丢，它的表现都稳居第一，而且数据缺失比例再怎么变都不带怕的。这项技术的代码已经公开，意味着智能手环的心率监测、工厂的设备故障预警、城市交通流量预测都会变得更精准可靠，未来你看到的健康报告或天气预测，背后的数据支撑将更扎实。

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.