A convolution recurrent autoencoder for spatio-temporal missing data imputation

Understand

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.