Dynamic Time Series Models using R-INLA

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.

• 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.

"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