Description
Book SynopsisDynamic 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.
Key Features:
- 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-
Trade Review
"This book will interest current R-users with a background in time series analyses who would like to expand their knowledge regarding INLA and its application with R-INLA package. This book also provides illustrative examples which can contribute to the understanding of the applications of these methods. This book can also benefit academic researchers who would like to apply these types of approaches in their fields."
Sébastien Bailly, French National Center for Medical Research (INSERM), France, ISCB, May 2023
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.
