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When sensors collect spatio-temporal data in a large geographical area, theexistence of missing data cannot be escaped. Missing data negatively impactsthe performance of data analysis and machine learning algorithms. In thispaper, we study deep autoencoders for missing data imputation inspatio-temporal problems. We propose a convolution bidirectional-LSTM forcapturing spatial and temporal patterns. Moreover, we analyze an autoencoder'slatent feature representation in spatio-temporal data and illustrate itsperformance for missing data imputation. Traffic flow data are used forevaluation of our models. The result shows that the proposed convolutionrecurrent neural network outperforms state-of-the-art methods.

A convolution recurrent autoencoder for spatio-temporal missing data imputation