Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework.
The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series.
- Introduction and overview of R-INLA for time series analysis.
- Gaussian and non-Gaussian state space models for time series.
- State space models for time series with exogenous predictors.
- Hierarchical models for a potentially large set of time series.
- Dynamic modelling of stochastic volatility and spatio-temporal dependence.
Table of Contents
Preface. 1. Bayesian Analysis. 2. A Review of INLA. 3. Modeling Univariate Time Series. 4. More Topics on DLMs with R-INLA. 5. Modeling Time Series with Exogenous Predictors. 6. Structural Time Series Decomposition using R-INLA. 7. Hierarchical DLM. 8. INLA for Multivariate Dynamic Models. 9. Modeling Binary Time Series. 10. Modeling Count Time Series. 11. Modeling Stochastic Volatility. 12. Comparison of R-INLA to Other Bayesian Alternatives. 13. Resources for the User.
Nalini Ravishanker is a professor in the Department of Statistics at the University of Connecticut, Storrs, USA.
Balaji Raman is a statistician at Cogitaas AVA, Mumbai, India.
Refik Soyer is a professor in the Department of Decision Sciences at The George Washington University, Washington D.C., USA.