A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics
<|reference_start|>A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics: 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.<|reference_end|>
@article{wang2024a,
    title={A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition
  Dynamics},
    author={Jiahao Wang, Sikun Yang, Heinz Koeppl, Xiuzhen Cheng, Pengfei Hu,
  Guoming Zhang},
    journal={arXiv preprint arXiv:2402.16297},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16297},
    primaryClass={cs.LG cs.AI}
}