Multivariate time series (MTS) prediction is ubiquitous in real-world fields, but MTS data often contains missing values. In recent years, there has been an increasing interest in using end-to-end models to handle MTS with missing values. To generate features for prediction, existing methods either merge all input dimensions of MTS or tackle each input dimension independently. However, both approaches are hard to perform well because the former usually produce many unreliable features and the latter lacks correlated information. In this paper, we propose a Learning Individual Features (LIFE) framework, which provides a new paradigm for MTS prediction with missing values. LIFE generates reliable features for prediction by using the correlated dimensions as auxiliary information and suppressing the interference from uncorrelated dimensions with missing values. Experiments on three real-world data sets verify the superiority of LIFE to existing state-of-the-art models.

翻译：多变时间序列(MTS)预测在现实世界领域普遍存在,但多边贸易体制数据往往包含缺失的数值。近年来,人们越来越有兴趣使用端到端模型来处理缺值的多边贸易体制。为了产生预测特征,现有方法要么将多边贸易体制的所有投入层面合并,要么独立处理每个输入层面。但是,这两种方法都很难运行良好,因为前者通常产生许多不可靠的特征,而后者缺乏相关信息。在本文中,我们提出了一个学习个体特征框架,为缺少值的多边贸易体制预测提供了新的范例。生命通过使用相关层面作为辅助信息并抑制与缺失值无关层面的干扰,产生了可靠的预测特征。关于三个现实世界数据集的实验证实了生命优于现有最先进的模型。