  Time-series data with missing values are commonly encountered in many fields,
such as healthcare, meteorology, and robotics. The imputation aims to fill the
missing values with valid values. Most imputation methods trained the models
implicitly because missing values have no ground truth. In this paper, we
propose Random Drop Imputation with Self-training (RDIS), a novel training
method for time-series data imputation models. In RDIS, we generate extra
missing values by applying a random drop on the observed values in incomplete
data. We can explicitly train the imputation models by filling in the randomly
dropped values. In addition, we adopt self-training with pseudo values to
exploit the original missing values. To improve the quality of pseudo values,
we set the threshold and filter them by calculating the entropy. To verify the
effectiveness of RDIS on the time series imputation, we test RDIS to various
imputation models and achieve competitive results on two real-world datasets.
