Time Series

Time Series: Modeling, Computation, and Inference

Series:
Published:
Content:
Author(s):
Free Standard Shipping

Purchasing Options

Hardback
$104.95 $83.96
ISBN 9781420093360
Cat# C9336
Add to cart
SAVE 20%
eBook (VitalSource)
$104.95 $73.47
ISBN 9781420093377
Cat# CE9336
Add to cart
SAVE 30%
eBook Rentals
 

Features

  • Covers the major areas of modern time series models and theory, including time and spectral domain and univariate and multivariate time series methods
  • Presents analyses of real time series data in numerous examples and case studies to illustrate the flexibility and practical impact of the models and methods
  • Emphasizes model-based, computationally intensive analysis of structured time series
  • Discusses recent techniques for modeling time series data, such as dynamic graphical models, SMC methods, and nonlinear/non-Gaussian dynamic models
  • Includes a collection of end-of-chapter exercises
  • Offers many of the data sets, R and MATLAB® code, and other material on the authors’ websites

Summary

Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling and analysis, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and emerging topics at research frontiers.

The book presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. The authors also explore the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian tools, such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. They illustrate the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, and finance. Data sets, R and MATLAB® code, and other material are available on the authors’ websites.

Along with core models and methods, this text offers sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application areas.

Table of Contents

Notation, Definitions, and Basic Inference
Problem areas and objectives
Stochastic processes and stationarity
Autocorrelation and cross-correlation functions
Smoothing and differencing
A primer on likelihood and Bayesian inference

Traditional Time Domain Models
Structure of autoregressions
Forecasting
Estimation in autoregressive (AR) models
Further issues on Bayesian inference for AR models
Autoregressive moving average (ARMA) models
Other models

The Frequency Domain
Harmonic regression
Some spectral theory
Discussion and extensions

Dynamic Linear Models
General linear model structures
Forecast functions and model forms
Inference in dynamic linear models (DLMs): basic normal theory
Extensions: non-Gaussian and nonlinear models
Posterior simulation: Markov chain Monte Carlo (MCMC) algorithms

State-Space Time-Varying Autoregressive Models
Time-varying autoregressions (TVAR) and decompositions
TVAR model specification and posterior inference
Extensions

Sequential Monte Carlo Methods for State-Space Models
General state-space models
Posterior simulation: sequential Monte Carlo (SMC)

Mixture Models in Time Series
Markov switching models
Multiprocess models
Mixtures of general state-space models
Case study: detecting fatigue from EEGs
Univariate stochastic volatility models

Topics and Examples in Multiple Time Series
Multichannel modeling of EEG data
Some spectral theory
Dynamic lag/lead models
Other approaches

Vector AR and ARMA Models
Vector AR (VAR) models
Vector ARMA (VARMA) models
Estimation in VARMA
Extensions: mixtures of VAR processes

Multivariate DLMs and Covariance Models
Theory of multivariate and matrix normal DLMs
Multivariate DLMs and exchangeable time series
Learning cross-series covariances
Time-varying covariance matrices
Multivariate dynamic graphical models

Author Index
Subject Index

Bibliography

Problems appear at the end of each chapter.


 

Author Bio(s)

Raquel Prado is an associate professor in the Department of Applied Mathematics and Statistics at the University of California, Santa Cruz.

Mike West is the Arts & Sciences Professor of Statistical Science in the Department of Statistical Science at Duke University.

Editorial Reviews

The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.
—Hsun-Hsien Chang, Computing Reviews, March 2012

My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.
—William Seaver, Technometrics, August 2011

… a very modern entry to the field of time-series modelling, with a rich reference list of the current literature, including 85 references from 2008 and later. It is well-written and I spotted very few typos. This textbook can undoubtedly work as a reference manual for anyone entering the field or looking for an update. … I am certain there is more than enough material within Time Series to fill an intense one-semester course.
International Statistical Review (2011), 79

 
Textbooks
Other CRC Press Sites
Featured Authors
STAY CONNECTED
Facebook Page for CRC Press Twitter Page for CRC Press You Tube Channel for CRC Press LinkedIn Page for CRC Press Google Plus Page for CRC Press Pinterest Page for CRC Press
Sign Up for Email Alerts
© 2014 Taylor & Francis Group, LLC. All Rights Reserved. Privacy Policy | Cookie Use | Shipping Policy | Contact Us