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Irregularly sampled time series are ubiquitous, presenting significantchallenges for analysis due to missing values. Despite existing methods addressimputation, 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, wepropose PRIME, a Prototype Recurrent Imputation ModEl, which integrates bothintra-series and inter-series information for imputing missing values inirregularly sampled time series. Our framework comprises a prototype memorymodule for learning inter-series information, a bidirectional gated recurrentunit utilizing prototype information for imputation, and an attentiveprototypical refinement module for adjusting imputations. We conductedextensive experiments on three datasets, and the results underscore PRIME'ssuperiority over the state-of-the-art models by up to 26% relative improvementon mean square error.

Imputation with Inter-Series Information from Prototypes for Irregular Sampled Time Series