2024/02/26 by Jiahao Wang, Sikun Yang, Wang, Jiahao +9
Earth and Planetary Sciences · Engineering · #Aquatic and Environmental Studies #Artificial Intelligence (cs.AI) #Elasticity and Wave Propagation #FOS: Computer and information sciences #Machine Learning (cs.LG) #Material Science and Thermodynamics
paper · pdf · doi:10.48550/arxiv.2402.16297
openalex publication_date 2024/02/26 · openalex created_date 2024/02/28 · openalex updated_date 2026/07/28
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 noisy and 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 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.