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Forecasting on sparse multivariate time series (MTS) aims to model thepredictors of future values of time series given their incomplete past, whichis important for many emerging applications. However, most existing methodsprocess MTS's individually, and do not leverage the dynamic distributionsunderlying the MTS's, leading to sub-optimal results when the sparsity is high.To address this challenge, we propose a novel generative model, which tracksthe transition of latent clusters, instead of isolated feature representations,to achieve robust modeling. It is characterized by a newly designed dynamicGaussian mixture distribution, which captures the dynamics of clusteringstructures, and is used for emitting timeseries. The generative model isparameterized by neural networks. A structured inference network is alsodesigned for enabling inductive analysis. A gating mechanism is furtherintroduced to dynamically tune the Gaussian mixture distributions. Extensiveexperimental results on a variety of real-life datasets demonstrate theeffectiveness of our method.

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series