Imputation with Inter-Series Information from Prototypes for Irregular
  Sampled Time Series
Zhihao Yu Xu Chu Liantao Ma Yasha Wang Wenwu Zhu
Irregularly sampled time series are ubiquitous, presenting significant
challenges for analysis due to missing values. Despite existing methods address
imputation, they predominantly focus on leveraging intra-series information,
neglecting the potential benefits that inter-series information could provide,
such as reducing uncertainty and memorization effect. To bridge this gap, we
propose PRIME, a Prototype Recurrent Imputation ModEl, which integrates both
intra-series and inter-series information for imputing missing values in
irregularly sampled time series. Our framework comprises a prototype memory
module for learning inter-series information, a bidirectional gated recurrent
unit utilizing prototype information for imputation, and an attentive
prototypical refinement module for adjusting imputations. We conducted
extensive experiments on three datasets, and the results underscore PRIME's
superiority over the state-of-the-art models by up to 26% relative improvement
on mean square error.