A convolution recurrent autoencoder for spatio-temporal missing data imputation
This addresses data quality issues for sensor-based applications like traffic monitoring, but it is incremental as it builds on existing autoencoder and spatio-temporal modeling techniques.
The paper tackles missing data imputation in spatio-temporal sensor data by proposing a convolution bidirectional-LSTM autoencoder, which outperforms state-of-the-art methods on traffic flow data.
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.