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Those who have the background necessary to use the R code and to replicate the results throughout the book will find plenty of material in this book to extend what they learn to their own data. "The book would be a good text for a seminar or a course on HMM or for self-learning the topic. Stationary Poisson HMM, numerical maximizationīivariate normal state-dependent distributionsĬategorical HMM, constrained optimizationįactorization needed for forward probabilitiesĬonditional independence of X t 1 and X T t+1Įxercises appear at the end of most chapters. Models for a heterogeneous group of subjectsĪpplication to caterpillar feeding behavior Parameter estimation by maximum likelihood Proportion in each of the five categories Models for the total number of deliveriesįirearm homicides as a proportion of all homicides, suicides, and legal intervention homicides

Multivariate HMM for returns on four shares Wind direction as classified into 16 categories Normal HMMs for durations and waiting timesīivariate model for durations and waiting times HMMs based on a second-order Markov chainīinary time series of short and long eruptions HMMs with general univariate state-dependent distribution Hidden Markov Models: Definition and PropertiesĮstimation by Direct Maximization of the Likelihoodįorecasting, Decoding, and State PredictionĪpplying the Gibbs sampler to Poisson HMMsīayesian estimation of the number of statesĮxtensions of the Basic Hidden Markov Model

It provides a broad understanding of the models and their uses. Hidden Markov Models for Time Series illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. They also provide R code for some of the examples, enabling the use of the codes in similar applications. Through examples and applications, the authors describe how to extend and generalize the basic model so it can be applied in a rich variety of situations. It also discusses how to employ the freely available computing environment R to carry out computations for parameter estimation, model selection and checking, decoding, and forecasting.Īfter presenting the simple Poisson HMM, Hidden Markov Models for Time Series covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference. Hidden Markov Models for Time Series applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts, and categorical observations.