This chapter considers various models that focus largely on serially dependent variables and the respective methodologies developed with a COM–Poisson underpinning. This chapter first introduces the reader to the various stochastic processes that have been established, including a homogeneous COM–Poisson process, a copula-based COM–Poisson Markov model, and a COM–Poisson hidden Markov model. Meanwhile, there are two approaches for conducting time series analysis on time-dependent count data. One approach assumes that the time dependence occurs with respect to the intensity vector. Under this framework, the usual time series models that assume a continuous variable can be applied. Alternatively, the time series model can be applied directly to the outcomes themselves. Maintaining the discrete nature of the observations, however, requires a different approach referred to as a thinning-based method. Different thinning-based operators can be considered for such models. The chapter then broadens the discussion of dependence to consider COM–Poisson-based spatio-temporal models, thus allowing both for serial and spatial dependence among variables.