  To analyze multivariate time series, most previous methods assume regular
subsampling of time series, where the interval between adjacent measurements
and the number of samples remain unchanged. Practically, data collection
systems could produce irregularly sampled time series due to sensor failures
and interventions. However, existing methods designed for regularly sampled
multivariate time series cannot directly handle irregularity owing to
misalignment along both temporal and variate dimensions. To fill this gap, we
propose Compatible Transformer (CoFormer), a transformer-based encoder to
achieve comprehensive temporal-interaction feature learning for each individual
sample in irregular multivariate time series. In CoFormer, we view each sample
as a unique variate-time point and leverage intra-variate/inter-variate
attentions to learn sample-wise temporal/interaction features based on
intra-variate/inter-variate neighbors. With CoFormer as the core, we can
analyze irregularly sampled multivariate time series for many downstream tasks,
including classification and prediction. We conduct extensive experiments on 3
real-world datasets and validate that the proposed CoFormer significantly and
consistently outperforms existing methods.
