ZUCCHINI, WALTER / MACDONALD, IAIN L / LANGROCK, ROLAND
u003cpu003eu003cbu003eHidden Markov Models for Time Series: An Introduction Using R, Second Editionu003c/bu003e 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.u003c/pu003e u003cpu003eAfter 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.u003c/pu003e u003cpu003eThe 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.u003c/pu003e u003cpu003eu003cbu003eFeaturesu003c/bu003eu003c/pu003e u003culu003e u003cliu003ePresents an accessible overview of HMMsu003c/liu003e u003cliu003eExplores a variety of applications in ecology, finance, epidemiology, climatology, and sociologyu003c/liu003e u003cliu003eIncludes numerous theoretical and programming exercisesu003c/liu003e u003cliu003eProvides most of the analysed data sets u003cbu003eonlineu003c/bu003eu003c/liu003e u003c/ulu003e u003cpu003eu003cbu003eNew to the second editionu003c/bu003eu003c/pu003e u003culu003e u003cliu003eA total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state processu003c/liu003e u003cliu003eNew case studies on animal movement, rainfall occurrence and capture-recapture datau003c/liu003e u003c/ulu003e