ABSTRACT
Hidden Markov models (HMMs) are models in which the distribution that generates an observation depends on the state of an underlying and unobserved Markov process. They show promise as flexible generalpurpose models for univariate and multivariate time series, especially for discrete-valued series, including categorical series and series of counts.
