Time Series (2^nd Ed.)
Modeling, Computation, and Inference, Second Edition
Chapman & Hall/CRC Texts in Statistical Science Series

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.

1. Notation, definitions, and basic inference
2. Traditional time domain models
3. The frequency domain
4. Dynamic linear models
5. State-space TVAR models
6. SMC methods for state-space models
7. Mixture models in time series
8. Topics and examples in multiple time series
9. Vector AR and ARMA models
10. General classes of multivariate dynamic models
11. Latent factor models

Raquel Prado is Professor in the Department of Statistics at the Baskin School of Engineering of the University of California Santa Cruz, USA. Her main research areas are time series analysis and Bayesian modeling - with a focus on analysis of large-dimensional nonstationary time series data and applications to biomedical signal processing and brain imaging. Marco A. R. Ferreira is an Associate Professor in the Department of Statistics at Virginia Tech, where he served from 2016 to 2020 as the Director of Graduate Programs. Mike West holds a Duke University distinguished chair as the Arts & Sciences Professor of Statistics & Decision Sciences in the Department of Statistical Science, where he led the development of statistics from 1990-2002.