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Time series forecasting using historical data has been an interesting andchallenging topic, especially when the data is corrupted by missing values. Inmany industrial problem, it is important to learn the inference functionbetween the auxiliary observations and target variables as it providesadditional knowledge when the data is not fully observed. We develop anend-to-end time series model that aims to learn the such inference relation andmake a multiple-step ahead forecast. Our framework trains jointly two neuralnetworks, one to learn the feature-wise correlations and the other for themodeling of temporal behaviors. Our model is capable of simultaneously imputingthe missing entries and making a multiple-step ahead prediction. Theexperiments show good overall performance of our framework over existingmethods in both imputation and forecasting tasks.

An End-to-End Time Series Model for Simultaneous Imputation and Forecast