  Time series forecasting using historical data has been an interesting and
challenging topic, especially when the data is corrupted by missing values. In
many industrial problem, it is important to learn the inference function
between the auxiliary observations and target variables as it provides
additional knowledge when the data is not fully observed. We develop an
end-to-end time series model that aims to learn the such inference relation and
make a multiple-step ahead forecast. Our framework trains jointly two neural
networks, one to learn the feature-wise correlations and the other for the
modeling of temporal behaviors. Our model is capable of simultaneously imputing
the missing entries and making a multiple-step ahead prediction. The
experiments show good overall performance of our framework over existing
methods in both imputation and forecasting tasks.
