Glocal Information Bottleneck for Time Series Imputation
Authors: name: Jie Yang, name: Kexin Zhang, name: Guibin Zhang, name: Philip S. Yu, name: Kaize Ding

Time Series Imputation (TSI), which aims to recover missing values in
temporal data, remains a fundamental challenge due to the complex and often
high-rate missingness in real-world scenarios. Existing models typically
optimize the point-wise reconstruction loss, focusing on recovering numerical
values (local information). However, we observe that under high missing rates,
these models still perform well in the training phase yet produce poor
imputations and distorted latent representation distributions (global
information) in the inference phase. This reveals a critical optimization
dilemma: current objectives lack global guidance, leading models to overfit
local noise and fail to capture global information of the data. To address this
issue, we propose a new training paradigm, Glocal Information Bottleneck
(Glocal-IB). Glocal-IB is model-agnostic and extends the standard IB framework
by introducing a Global Alignment loss, derived from a tractable mutual
information approximation. This loss aligns the latent representations of
masked inputs with those of their originally observed counterparts. It helps
the model retain global structure and local details while suppressing noise
caused by missing values, giving rise to better generalization under high
missingness. Extensive experiments on nine datasets confirm that Glocal-IB
leads to consistently improved performance and aligned latent representations
under missingness. Our code implementation is available in
https://github.com/Muyiiiii/NeurIPS-25-Glocal-IB.