Hidden Markov Models for Time Series: An Introduction Using R.

Walter Zucchini, Iain L. MacDonald

ISBN 10: 1584885734 / ISBN 13: 9781584885733

Published by CRC Press, 2009

About this Item

Ex library with typical labeling on spine and cover, one stamp on inside front cover; virtually like new inside otherwise. Some rubbing on covers.

Seller Inventory # 1127067

Bibliographic Details

• Title: Hidden Markov Models for Time Series: An Introduction Using R.
• Publisher: CRC Press
• Publication Date: 2009
• Language: English
• ISBN 10: 1584885734
• ISBN 13: 9781584885733
• Binding: Hard cover
• Condition: Good
• Dust Jacket Condition: No jacket
• Seller catalogs:
  • Mathematics

About this title

Synopsis

Reveals How HMMs Can Be Used as General-Purpose Time Series Models

Implements all methods in R
Hidden Markov Models for Time Series: An Introduction Using R 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. 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.

Illustrates the methodology in action
After presenting the simple Poisson HMM, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference. 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. They also provide R code for some of the examples, enabling the use of the codes in similar applications.

Effectively interpret data using HMMs
This book illustrates the wonderful flexibility of HMMs as general-purpose models for time series data. It provides a broad understanding of the models and their uses.

About the Author

University of Gottingen, Germany University of Cape Town, South Africa University College, London, UK Stanford University, California, USA Johns Hopkins Bloomberg School of Public Health, MD, USA London School of Economics, UK London School of Economics, UK University of Copenhagen, Denmark