LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values
This addresses the challenge of handling missing values in multivariate time series prediction for applications in real-world fields, representing a new paradigm rather than an incremental improvement.
The paper tackles the problem of multivariate time series prediction with missing values by proposing the LIFE framework, which generates reliable features using correlated dimensions as auxiliary information and suppressing interference from uncorrelated dimensions, achieving superior performance over state-of-the-art models on three real-world datasets.
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.