  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.
