Structure-aware decoupled imputation network for multivariate time series

Handling incomplete multivariate time series is an important and fundamental concern for a variety of domains. Existing time-series imputation approaches rely on basic assumptions regarding relationship information between sensors, posing significant challenges since inter-sensor interactions in the real world are often complex and unknown beforehand. Specifically, there is a lack of in-depth investigation into (1) the coexistence of relationships between sensors and (2) the incorporation of reciprocal impact between sensor properties and inter-sensor relationships for the time-series imputation problem. To fill this gap, we present the Structure-aware Decoupled imputation network (SaD), which is designed to model sensor characteristics and relationships between sensors in distinct latent spaces. Our approach is equipped with a two-step knowledge integration scheme that incorporates the influence between the sensor attribute information as well as sensor relationship information. The experimental results indicate that when compared to state-of-the-art models for time-series imputation tasks, our proposed method can reduce error by around 15%.