Glocal Information Bottleneck for Time Series Imputation
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.
68e6813a975ac4c405ef238a
Jie Yang
claimed_verified
2025-10-09T06:51:11.490Z
64b0f081698046a2f2d3951f
/avatars/26f7a4759ddcda6bdb6b9a770d482546.svg
Jie Yang
user
Muyiiiii
68e6813a975ac4c405ef238b
Kexin Zhang
68e6813a975ac4c405ef238c
Guibin Zhang
68e6813a975ac4c405ef238d
Philip S. Yu
68e6813a975ac4c405ef238e
Kaize Ding
Glocal Information Bottleneck for Time Series Imputation
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.
A new training paradigm, Glocal Information Bottleneck, improves time series imputation by aligning latent representations to retain global structure and local details under high missingness.
Time Series Imputation Glocal Information Bottleneck Global Alignment loss mutual information approximation latent representations overfitting local noise generalization