Dynamic time series models using R-INLA : an applied perspective

Table of Contents: 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

This book is the outcome of a joint effort to systematically describe the use of R-INLA for analyzing 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.