Hidden Markov Models for Time Series

An Introduction Using R, Second Edition

Author: Walter Zucchini,Iain L. MacDonald,Roland Langrock

Publisher: CRC Press

ISBN: 1482253844

Category: Mathematics

Page: 370

View: 7393

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. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture–recapture data

Hidden Markov Models for Time Series

An Introduction Using R, Second Edition

Author: Walter Zucchini,Iain L. MacDonald,Roland Langrock

Publisher: CRC Press

ISBN: 1315355205

Category: Mathematics

Page: 370

View: 9805

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. Features Presents an accessible overview of HMMs Explores a variety of applications in ecology, finance, epidemiology, climatology, and sociology Includes numerous theoretical and programming exercises Provides most of the analysed data sets online New to the second edition A total of five chapters on extensions, including HMMs for longitudinal data, hidden semi-Markov models and models with continuous-valued state process New case studies on animal movement, rainfall occurrence and capture–recapture data

Hidden Markov Models for Time Series

An Introduction Using R

Author: Walter Zucchini,Iain L. MacDonald

Publisher: CRC Press

ISBN: 9781420010893

Category: Mathematics

Page: 288

View: 1147

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.

Hidden Markov and Other Models for Discrete- valued Time Series

Author: Iain L. MacDonald,Walter Zucchini

Publisher: CRC Press

ISBN: 9780412558504

Category: Mathematics

Page: 256

View: 4018

Discrete-valued time series are common in practice, but methods for their analysis are not well-known. In recent years, methods have been developed which are specifically designed for the analysis of discrete-valued time series. Hidden Markov and Other Models for Discrete-Valued Time Series introduces a new, versatile, and computationally tractable class of models, the "hidden Markov" models. It presents a detailed account of these models, then applies them to data from a wide range of diverse subject areas, including medicine, climatology, and geophysics. This book will be invaluable to researchers and postgraduate and senior undergraduate students in statistics. Researchers and applied statisticians who analyze time series data in medicine, animal behavior, hydrology, and sociology will also find this information useful.

Inference in Hidden Markov Models

Author: Olivier Cappé,Eric Moulines,Tobias Ryden

Publisher: Springer Science & Business Media

ISBN: 0387289828

Category: Mathematics

Page: 653

View: 8973

This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.

Latent Markov Models for Longitudinal Data

Author: Francesco Bartolucci,Alessio Farcomeni,Fulvia Pennoni

Publisher: CRC Press

ISBN: 1466583711

Category: Mathematics

Page: 252

View: 5310

Drawing on the authors’ extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data focuses on the formulation of latent Markov models and the practical use of these models. Numerous examples illustrate how latent Markov models are used in economics, education, sociology, and other fields. The R and MATLAB® routines used for the examples are available on the authors’ website. The book provides you with the essential background on latent variable models, particularly the latent class model. It discusses how the Markov chain model and the latent class model represent a useful paradigm for latent Markov models. The authors illustrate the assumptions of the basic version of the latent Markov model and introduce maximum likelihood estimation through the Expectation-Maximization algorithm. They also cover constrained versions of the basic latent Markov model, describe the inclusion of the individual covariates, and address the random effects and multilevel extensions of the model. After covering advanced topics, the book concludes with a discussion on Bayesian inference as an alternative to maximum likelihood inference. As longitudinal data become increasingly relevant in many fields, researchers must rely on specific statistical and econometric models tailored to their application. A complete overview of latent Markov models, this book demonstrates how to use the models in three types of analysis: transition analysis with measurement errors, analyses that consider unobserved heterogeneity, and finding clusters of units and studying the transition between the clusters.

Hidden Markov Models

Estimation and Control

Author: Robert J Elliott,Lakhdar Aggoun,John B. Moore

Publisher: Springer Science & Business Media

ISBN: 0387848541

Category: Science

Page: 382

View: 6573

As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics. In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors’ general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control.

Multivariate Statistical Modelling Based on Generalized Linear Models

Author: Ludwig Fahrmeir,Gerhard Tutz

Publisher: Springer Science & Business Media

ISBN: 1489900101

Category: Mathematics

Page: 426

View: 1597

Concerned with the use of generalised linear models for univariate and multivariate regression analysis, this is a detailed introductory survey of the subject, based on the analysis of real data drawn from a variety of subjects such as the biological sciences, economics, and the social sciences. Where possible, technical details and proofs are deferred to an appendix in order to provide an accessible account for non-experts. Topics covered include: models for multi-categorical responses, model checking, time series and longitudinal data, random effects models, and state-space models. Throughout, the authors have taken great pains to discuss the underlying theoretical ideas in ways that relate well to the data at hand. As a result, numerous researchers whose work relies on the use of these models will find this an invaluable account.

Python Machine Learning Cookbook

Author: Prateek Joshi

Publisher: Packt Publishing Ltd

ISBN: 1786467682

Category: Computers

Page: 304

View: 9188

100 recipes that teach you how to perform various machine learning tasks in the real world About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide Learn about perceptrons and see how they are used to build neural networks Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques Who This Book Is For This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code. What You Will Learn Explore classification algorithms and apply them to the income bracket estimation problem Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Explore data visualization techniques to interact with your data in diverse ways Find out how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Analyze stock market data using Conditional Random Fields Work with image data and build systems for image recognition and biometric face recognition Grasp how to use deep neural networks to build an optical character recognition system In Detail Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples. Style and approach You will explore various real-life scenarios in this book where machine learning can be used, and learn about different building blocks of machine learning using independent recipes in the book.

Hidden Markov Models for Time Series

An Introduction Using R

Author: Walter Zucchini,Iain L. MacDonald

Publisher: Chapman and Hall/CRC

ISBN: 9781584885733

Category: Mathematics

Page: 288

View: 8940

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.

Hidden Markov Models in Finance

Author: Rogemar S. Mamon,Robert J Elliott

Publisher: Springer Science & Business Media

ISBN: 0387711635

Category: Business & Economics

Page: 186

View: 6847

A number of methodologies have been employed to provide decision making solutions globalized markets. Hidden Markov Models in Finance offers the first systematic application of these methods to specialized financial problems: option pricing, credit risk modeling, volatility estimation and more. The book provides tools for sorting through turbulence, volatility, emotion, chaotic events – the random "noise" of financial markets – to analyze core components.

Hidden Markov Models for Time Series

An Introduction Using R

Author: Walter Zucchini,Iain L. MacDonald

Publisher: Chapman and Hall/CRC

ISBN: 9781584885733

Category: Mathematics

Page: 288

View: 6249

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.

Hidden Semi-Markov Models

Theory, Algorithms and Applications

Author: Shun-Zheng Yu

Publisher: Morgan Kaufmann

ISBN: 0128027711

Category: Computers

Page: 208

View: 7727

Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science. Discusses the latest developments and emerging topics in the field of HSMMs Includes a description of applications in various areas including, Human Activity Recognition, Handwriting Recognition, Network Traffic Characterization and Anomaly Detection, and Functional MRI Brain Mapping. Shows how to master the basic techniques needed for using HSMMs and how to apply them.

Between Data Science and Applied Data Analysis

Proceedings of the 26th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Mannheim, July 22–24, 2002

Author: Martin Schader,Wolfgang Gaul,Maurizio Vichi

Publisher: Springer Science & Business Media

ISBN: 3642189911

Category: Computers

Page: 694

View: 4851

The volume presents new developments in data analysis and classification and gives an overview of the state of the art in these scientific fields and relevant applications. Areas that receive considerable attention in the book are clustering, discrimination, data analysis, and statistics, as well as applications in economics, biology, and medicine it provides recent technical and methodological developments and a large number of application papers demonstrating the usefulness of the newly developed techniques.

Markov Models for Pattern Recognition

From Theory to Applications

Author: Gernot A. Fink

Publisher: Springer Science & Business Media

ISBN: 1447163087

Category: Computers

Page: 276

View: 7430

This thoroughly revised and expanded new edition now includes a more detailed treatment of the EM algorithm, a description of an efficient approximate Viterbi-training procedure, a theoretical derivation of the perplexity measure and coverage of multi-pass decoding based on n-best search. Supporting the discussion of the theoretical foundations of Markov modeling, special emphasis is also placed on practical algorithmic solutions. Features: introduces the formal framework for Markov models; covers the robust handling of probability quantities; presents methods for the configuration of hidden Markov models for specific application areas; describes important methods for efficient processing of Markov models, and the adaptation of the models to different tasks; examines algorithms for searching within the complex solution spaces that result from the joint application of Markov chain and hidden Markov models; reviews key applications of Markov models.

Dynamic Process Methodology in the Social and Developmental Sciences

Author: Jaan Valsiner,Peter C. M. Molenaar,Maria C.D.P. Lyra,Nandita Chaudhary

Publisher: Springer Science & Business Media

ISBN: 9780387959221

Category: Psychology

Page: 668

View: 3778

All psychological processes—like biological and social ones—are dynamic. Phenomena of nature, society, and the human psyche are context bound, constantly changing, and variable. This feature of reality is often not recognized in the social sciences where we operate with averaged data and with homogeneous stereotypes, and consider our consistency to be the cornerstone of rational being. Yet we are all inconsistent in our actions within a day, or from, one day to the next, and much of such inconsistency is of positive value for our survival and development. Our inconsistent behaviors and thoughts may appear chaotic, yet there is generality within this highly variable dynamic. The task of scientific methodologies—qualitative and quantitative—is to find out what that generality is. It is the aim of this handbook to bring into one framework various directions of construction of methodology of the dynamic processes that exist in the social sciences at the beginning of the 21st century. This handbook is set up to bring together pertinent methodological scholarship from all over the world, and equally from the quantitative and qualitative orientations to methodology. In addition to consolidating the pertinent knowledge base for the purposes of its further growth, this book serves the major educational role of bringing practitioners—students, researchers, and professionals interested in applications—the state of the art know-how about how to think about extracting evidence from single cases, and about the formal mathematical-statistical tools to use for these purposes.

Data Warehousing and Knowledge Discovery

11th International Conference, DaWaK 2009 Linz, Austria, August 31-September 2, 2009 Proceedings

Author: Torben Bach Pedersen,Mukesh K. Mohania,A Min Tjoa

Publisher: Springer Science & Business Media

ISBN: 3642037291

Category: Computers

Page: 480

View: 9615

This book constitutes the refereed proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2009 held in Linz, Austria in August/September 2009. The 36 revised full papers presented were carefully reviewed and selected from 124 submissions. The papers are organized in topical sections on data warehouse modeling, data streams, physical design, pattern mining, data cubes, data mining applications, analytics, data mining, clustering, spatio-temporal mining, rule mining, and OLAP recommendation.

Finite Mixture and Markov Switching Models

Author: Sylvia Frühwirth-Schnatter

Publisher: Springer Science & Business Media

ISBN: 0387357688

Category: Mathematics

Page: 494

View: 8123

The past decade has seen powerful new computational tools for modeling which combine a Bayesian approach with recent Monte simulation techniques based on Markov chains. This book is the first to offer a systematic presentation of the Bayesian perspective of finite mixture modelling. The book is designed to show finite mixture and Markov switching models are formulated, what structures they imply on the data, their potential uses, and how they are estimated. Presenting its concepts informally without sacrificing mathematical correctness, it will serve a wide readership including statisticians as well as biologists, economists, engineers, financial and market researchers.