  Forecasting on sparse multivariate time series (MTS) aims to model the
predictors of future values of time series given their incomplete past, which
is important for many emerging applications. However, most existing methods
process MTS's individually, and do not leverage the dynamic distributions
underlying the MTS's, leading to sub-optimal results when the sparsity is high.
To address this challenge, we propose a novel generative model, which tracks
the transition of latent clusters, instead of isolated feature representations,
to achieve robust modeling. It is characterized by a newly designed dynamic
Gaussian mixture distribution, which captures the dynamics of clustering
structures, and is used for emitting timeseries. The generative model is
parameterized by neural networks. A structured inference network is also
designed for enabling inductive analysis. A gating mechanism is further
introduced to dynamically tune the Gaussian mixture distributions. Extensive
experimental results on a variety of real-life datasets demonstrate the
effectiveness of our method.
