# Recover Missing Sensor Data with Iterative Imputing Network

arXiv: https://arxiv.org/abs/1711.07878 · HTML: https://mlsanity.com/paper/1711.07878/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2017.

## Built on by (most cited first, 2 of 2)

- [Network of Tensor Time Series](https://mlsanity.com/paper/2102.07736/) (2021)
- [Imputing Missing Observations with Time Sliced Synthetic Minority Oversampling Technique](https://mlsanity.com/paper/2201.05634/) (2022)

## Abstract (arXiv)

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.