Focusing on Bayesian approaches and computations using analytic and
simulation-based methods for inference,
Time Series: Modeling, Computation, and Inference, Second Edition
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, analysis and forecasting, a broad range of
references to state-of-the-art approaches to univariate and multivariate
time series analysis, and contacts research frontiers in multivariate
time series modeling and forecasting.
It presents overviews of several classes of models and related
methodology for inference, statistical computation for model fitting and
assessment, and forecasting. It explores the connections between time-
and frequency-domain approaches and develop various models and analyses
using Bayesian formulations and computation, including use of
computations based on Markov chain Monte Carlo (MCMC) and sequential
Monte Carlo (SMC) methods. It illustrates the models and methods with
examples and case studies from a variety of fields, including signal
processing, biomedicine, environmental science, and finance.
Along with core models and methods, the book represents state-of-the art
approaches to analysis and forecasting in challenging time series
problems. It also demonstrates the growth of time series analysis into
new application areas in recent years, and contacts recent and relevant
modeling developments and research challenges.
New in the second edition:
Expanded on aspects of core model theory and methodology.
Multiple new examples and exercises.
Detailed development of dynamic factor models.
Updated discussion and connections with recent and current research
frontiers.
