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Hidden Markov Models For Time Series: An Introduction Using R, Second
Edition
Hidden Markov Models for Time Series An Introduction Using R Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data The book provides a broad understanding of the models and their uses After presenting the basic model formulation the book covers estimation forecasting decoding prediction model selection and Bayesian inference for HMMs Through examples and applications the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations The book demonstrates how HMMs can be applied to a wide range of types of time series continuous-valued circular multivariate binary bounded and unbounded counts and categorical observations It also discusses how to employ the freely
available computing environment R to carry out the computations FeaturesPresents an accessible overview of HMMsExplores a variety of applications in ecology finance epidemiology climatology and sociologyIncludes numerous theoretical and programming exercisesProvides most of the analysed data sets onlineNew to the second editionA total of five chapters on extensions including HMMs for longitudinal data hidden semi-Markov models and models with continuous-valued state processNew case studies on animal movement rainfall occurrence and capture-recapture data
available computing environment R to carry out the computations FeaturesPresents an accessible overview of HMMsExplores a variety of applications in ecology finance epidemiology climatology and sociologyIncludes numerous theoretical and programming exercisesProvides most of the analysed data sets onlineNew to the second editionA total of five chapters on extensions including HMMs for longitudinal data hidden semi-Markov models and models with continuous-valued state processNew case studies on animal movement rainfall occurrence and capture-recapture data
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