Learning representations of multivariate time series with missing data

Highlights：

• We design a recurrent autoencoder architecture to compress multivariate time series with missing data.

• An additional regularization term aligns the learned representations with a prior kernel, which accounts for missing data.

• Even with many missing data, time series belonging to different classes become well separated in the induced latent space.

• We exploit the proposed architecture to design methods for anomaly detection and for imputing missing data.

• We perform an analysis to investigate which kind of time series can be effectively encoded using recurrent layers.