Imputations of missing values using a tracking-removed autoencoder trained with incomplete data.

•Incomplete data are modeled based on the autoencoder for imputations of missing values.•A tracking-removed autoencoder is proposed by redesigning the input structure of the traditional hidden neurons in a dynamic way.•Missing values are treated as variables so as to participate in the network training.