NRTSI: Non-Recurrent Time Series Imputation

Abstract

Time series imputation is a fundamental task in understanding sequential data. Existing methods either rely on recurrent models that suffer heavily from error compounding or fail to exploit the hierarchical information of temporal data, both of which degrade performance severely with sparsely observed data. In this work, we reformulate time series as sets and propose a novel non-recurrent imputation model, Non-Recurrent Time Series Imputation (NRTSI), that does not impose any recurrent structures. Taking advantage of the set formulation, we design a principled and efficient hierarchical imputation procedure. In addition, NRTSI can perform multiple-mode stochastic imputation, directly handle irregularly-sampled time series, and handle data with partially observed dimensions. Empirically, we show that NRTSI achieves state-of-the-art performance on multiple benchmarks.