  Missing data in time series is a challenging issue affecting time series
analysis. Missing data occurs due to problems like data drops or sensor
malfunctioning. Imputation methods are used to fill in these values, with
quality of imputation having a significant impact on downstream tasks like
classification. In this work, we propose a semi-supervised imputation method,
ST-Impute, that uses both unlabeled data along with downstream task's labeled
data. ST-Impute is based on sparse self-attention and trains on tasks that
mimic the imputation process. Our results indicate that the proposed method
outperforms the existing supervised and unsupervised time series imputation
methods measured on the imputation quality as well as on the downstream tasks
ingesting imputed time series.
