A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics
This work addresses a specific gap in probabilistic modeling for noisy and incomplete count data, offering an incremental improvement over existing PGDS methods.
The paper tackled the limitation of existing Poisson-Gamma Dynamical Systems (PGDSs) in capturing time-varying transition dynamics in count-valued time sequences, proposing a non-stationary PGDS that models evolving transition matrices with Dirichlet Markov chains and achieves improved predictive performance compared to related models.
Probabilistic approaches for handling count-valued time sequences have attracted amounts of research attentions because their ability to infer explainable latent structures and to estimate uncertainties, and thus are especially suitable for dealing with \emph{noisy} and \emph{incomplete} count data. Among these models, Poisson-Gamma Dynamical Systems (PGDSs) are proven to be effective in capturing the evolving dynamics underlying observed count sequences. However, the state-of-the-art PGDS still fails to capture the \emph{time-varying} transition dynamics that are commonly observed in real-world count time sequences. To mitigate this gap, a non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time, and the evolving transition matrices are modeled by sophisticatedly-designed Dirichlet Markov chains. Leveraging Dirichlet-Multinomial-Beta data augmentation techniques, a fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance due to its capacity to learn non-stationary dependency structure captured by the time-evolving transition matrices.