# A convolution recurrent autoencoder for spatio-temporal missing data imputation

arXiv: https://arxiv.org/abs/1904.12413 · HTML: https://mlsanity.com/paper/1904.12413/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

## Built on by (most cited first, 6 of 6)

- [Dynamic Spatiotemporal Graph Convolutional Neural Networks for Traffic Data Imputation with Complex Missing Patterns](https://mlsanity.com/paper/2109.08357/) (2021)
- [A Deep Learning Framework for Traffic Data Imputation Considering Spatiotemporal Dependencies](https://mlsanity.com/paper/2304.09182/) (2023)
- [Spatio-Temporal Contrastive Self-Supervised Learning for POI-level Crowd Flow Inference](https://mlsanity.com/paper/2309.03239/) (2023)
- [Clustering of Time Series Data with Prior Geographical Information](https://mlsanity.com/paper/2107.01310/) (2021)
- [A Latent Feature Analysis-based Approach for Spatio-Temporal Traffic Data Recovery](https://mlsanity.com/paper/2208.07739/) (2022)
- [Large-Scale Traffic Data Imputation with Spatiotemporal Semantic Understanding](https://mlsanity.com/paper/2301.11691/) (2023)

## Abstract (arXiv)

When sensors collect spatio-temporal data in a large geographical area, the existence of missing data cannot be escaped. Missing data negatively impacts the performance of data analysis and machine learning algorithms. In this paper, we study deep autoencoders for missing data imputation in spatio-temporal problems. We propose a convolution bidirectional-LSTM for capturing spatial and temporal patterns. Moreover, we analyze an autoencoder's latent feature representation in spatio-temporal data and illustrate its performance for missing data imputation. Traffic flow data are used for evaluation of our models. The result shows that the proposed convolution recurrent neural network outperforms state-of-the-art methods.