Dynamic Time Series Models using R-INLA

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.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-temporal dependence.

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.