Dynamic Time Series Models using R-INLA: An Applied Perspective

Dynamic Time Series Models using R-INLA: An Applied Perspective is a book that provides an introduction to R-INLA for time series analysis, covering Gaussian and non-Gaussian state space models, hierarchical models, and dynamic modelling of stochastic volatility and spatio-temporal dependence. It is an ideal reference for statisticians and scientists working with time series data and is suitable for teaching a course on Bayesian analysis using state space models for time series.

Format: Hardback
Length: 282 pages
Publication date: 05 August 2022
Publisher: Taylor & Francis Ltd

Dynamic Time Series Models using R-INLA: An Applied Perspective is the culmination of a collaborative endeavor aimed at providing a comprehensive and systematic account of the application of R-INLA for analyzing time series data. This book serves as a valuable resource for both statisticians and scientists engaged in time series analysis, offering a comprehensive introduction to R-INLA and the necessary tools for modeling diverse types of time series utilizing an approximate Bayesian framework.

The book is particularly well-suited as a reference for professionals working with time series data, providing a comprehensive and authoritative guide to the field. Furthermore, it serves as an excellent teaching resource for courses focused on Bayesian analysis using state space models for time series.

Key Features:

Introduction and Overview of R-INLA for Time Series Analysis: This chapter provides a comprehensive introduction to R-INLA, outlining its key features, advantages, and applications in time series analysis. It covers the basic principles of R-INLA, including its implementation, model fitting, and inference procedures.

Gaussian and Non-Gaussian State Space Models for Time Series: The book delves into the development of Gaussian and non-Gaussian state space models for time series analysis. It discusses the advantages and limitations of these models, as well as the techniques used to estimate and validate them. Examples of both linear and nonlinear state space models are provided, showcasing their applications in various fields.

State Space Models for Time Series with Exogenous Predictors: This chapter explores the use of state space models for time series with exogenous predictors. It discusses the estimation of exogenous variables, the incorporation of exogenous predictors into the state space model, and the analysis of the resulting model. Examples of applications in finance, economics, and meteorology are provided to illustrate the practical implications of this approach.

Hierarchical Models for a Potentially Large Set of Time Series: The book introduces hierarchical models, which are useful when dealing with a large set of time series. It discusses the construction of hierarchical models, the estimation of parameters, and the inference of the model. Examples of applications in genetics, epidemiology, and environmental science are provided to demonstrate the versatility of hierarchical models.

Dynamic Modeling of Stochastic Volatility and Spatio-Temporal Dependence: This chapter focuses on the dynamic modeling of stochastic volatility and spatio-temporal dependence. It discusses the development of dynamic models for capturing the dynamics of financial time series, as well as the estimation of parameters and the analysis of the resulting model. Examples of applications in finance and economics are provided to illustrate the practical benefits of dynamic modeling.

In conclusion, Dynamic Time Series Models using R-INLA: An Applied Perspective is a comprehensive and authoritative guide to the application of R-INLA for analyzing time series data. It provides a solid foundation for both novice and experienced practitioners in the field, covering a wide range of topics and providing practical examples to illustrate the theoretical concepts. Whether you are a statistician, scientist, or educator, this book is an essential resource for advancing your understanding of time series analysis and its practical applications.

Dimension: 254 x 178 (mm)
ISBN-13: 9780367654276