  Sensor data has been playing an important role in machine learning tasks,
complementary to the human-annotated data that is usually rather costly.
However, due to systematic or accidental mis-operations, sensor data comes very
often with a variety of missing values, resulting in considerable difficulties
in the follow-up analysis and visualization. Previous work imputes the missing
values by interpolating in the observational feature space, without consulting
any latent (hidden) dynamics. In contrast, our model captures the latent
complex temporal dynamics by summarizing each observation's context with a
novel Iterative Imputing Network, thus significantly outperforms previous work
on the benchmark Beijing air quality and meteorological dataset. Our model also
yields consistent superiority over other methods in cases of different missing
rates.
