An End-to-End Time Series Model for Simultaneous Imputation and Forecast
This addresses forecasting challenges in industrial applications where data is often incomplete, though it appears incremental as it builds on existing neural network methods.
The paper tackles the problem of time series forecasting with missing data by developing an end-to-end model that simultaneously imputes missing values and makes multi-step predictions, showing good overall performance in experiments.
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.