We further generalize the proposed model by replacing the Gaussian observation layer with the negative binomial distribution to model multivariate count time series. In each sub-sequence, different data dimensions often share similar temporal patterns but may exhibit distinct magnitudes, and hence allowing the superposition of all sub-sequences to exhibit diverse behaviors at different data dimensions. For temporal pattern discovery, the latent representation under the model is used to decompose the time series into a parsimonious set of multivariate sub-sequences. We introduce graph gamma process (GGP) linear dynamical systems to model real-valued multivariate time series.
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