Negative-Binomial Randomized Gamma Markov Processes for Heterogeneous Overdispersed Count Time Series

arXiv:2402.18995v1 Announce Type: new
Abstract: Modeling count-valued time series has been receiving increasing attention since count time series naturally arise in physical and social domains. Poisson gamma dynamical systems (PGDSs) are newly-developed methods, which can well capture the expressive latent transition structure and bursty dynamics behind count sequences. In particular, PGDSs demonstrate superior performance in terms of data imputation and prediction, compared with canonical linear dynamical system (LDS) based methods. Despite these advantages, PGDS cannot capture the heterogeneous overdispersed behaviours …

abstract arxiv attention binomial count cs.ai cs.lg domains dynamics markov modeling negative negative-binomial processes series social stat.ml systems time series transition type