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.

翻译：利用历史数据进行时间序列预测一直是一个有趣且具有挑战性的课题，尤其是在数据受到缺失值污染的情况下。在许多工业问题中，学习辅助观测值与目标变量之间的推断函数至关重要，因为这能在数据未被完全观测时提供额外知识。我们开发了一种端到端时间序列模型，旨在学习这种推断关系并进行多步超前预测。我们的框架联合训练两个神经网络：一个用于学习特征间相关性，另一个用于建模时序行为。该模型能够同时插补缺失条目并进行多步超前预测。实验结果表明，在插补与预测任务中，我们的框架整体性能优于现有方法。