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.
Table of Contents
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.
Raquel leads/has led NSF and NIH funded projects, including multi-institutional and multi- disciplinary collaborative projects. She has supervised over 20 graduate students at UCSC and other academic institutions. Her former students work in academia, high tech companies, national laboratories and local government agencies. Raquel is past president of the International Society for Bayesian Analysis (ISBA). She is an ISBA fellow and a fellow of the American Statistical Association (ASA). She has served on several committees at ASA and ISBA and is currently a member of the Committee on Applied and Theoretical Statistics (CATS) of the National Academies of Sciences, Engineering and Medicine.
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. Marco has served the statistics profession in editorial boards of multiple scientific journals including the journal Bayesian Analysis, in several committees of ISBA and ASA, as well as in scientific committees of numerous domestic and international conferences.
Marco’s current research areas include dynamic models for time series and spatiotemporal data, multiscale models, objective Bayesian methods, stochastic search algorithms, and statistical computation. Major areas of application include bioinformatics, finance, and environmental science. Marco’s research has been, and is, funded by grants from the National Science Foundation. Marco has advised over 10 PhD students and postdocs and has published over 50 scientific papers. His former students and postdocs work in academic, industrial, and governmental positions.
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. A past president of the International Society for Bayesian Analysis (ISBA), Mike has served the international statistics profession in founding roles for ISBA and in other professional organisations and institutions.
Mike’s research and teaching activities are in Bayesian analysis in ranges of interlinked areas: theory and methods of dynamic models in time series analysis, multivariate analysis, latent structure, high-dimensional inference and computation, quantitative and computational decision analysis, stochastic computational methods, and statistical computing, among other topics. Interdisciplinary R&D has ranged across applications in signal processing, finance, econometrics, climatology, systems biology, genomics and neuroscience, among other areas. Main current interests are in macroeconomic forecasting and policy decisions, financial econometric forecasting and decisions, dynamic network studies in IT/commerce, and large-scale forecasting and decision problems in business and industry.
Mike has received a number of international awards for research and professional service, and multiple distinguished speaking awards. He has been, and is, a statistical consultant for various companies, banks, government agencies and academic centers, co-founder of a biotech company, and past or current advisor or board member for several financial and IT companies. Mike teaches in academia and through short-courses, works with and advises many undergraduates and Master’s students, and has mentored over 60 primary PhD students and postdoctoral associates, most of whom are now in academic, industrial or governmental positions involving advanced statistical research.