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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 veryoften with a variety of missing values, resulting in considerable difficultiesin the follow-up analysis and visualization. Previous work imputes the missingvalues by interpolating in the observational feature space, without consultingany latent (hidden) dynamics. In contrast, our model captures the latentcomplex temporal dynamics by summarizing each observation's context with anovel Iterative Imputing Network, thus significantly outperforms previous workon the benchmark Beijing air quality and meteorological dataset. Our model alsoyields consistent superiority over other methods in cases of different missingrates.

Recover Missing Sensor Data with Iterative Imputing Network