Bayesian Forecasting and Dynamic Models (Springer Series in Statistics)

The second edition of this book includes revised, updated, and additional material on the structure, theory, and application of classes of dynamic models in Bayesian time series analysis and forecasting. In addition to wide ranging updates to central material in the first edition, the second edition includes many more exercises and covers new topics at the research and application frontiers of Bayesian forecastings. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. Much progress has been made with mathematical and statistical aspects of forecasting models and related techniques, and experience has been gained through application in a variety of areas in commercial and industrial, scientific and socio-economic fields. Indeed much of the technical development has been driven by the needs of forecasting practitioners. There now exists a relatively complete statistical and mathematical framework that is described and illustrated here for the first time in book form, presenting our view of this approach to modelling and forecasting. The book provides a self-contained text for advanced university students and research workers in business, economic and scientific disciplines, and forecasting practitioners. The material covers mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each chapter. In order that the ideas and techniques of Bayesian forecasting be accessible to students, research workers and practitioners alike, the book includes a number of examples and case studies involving real data, generously illustrated using computer generated graphs. These examples provide issues of modelling, data analysis and forecasting. The Second Edition Of This Book Includes Revised, Updated, And Additional Material On The Structure, Theory, And Application Of Classes Of Dynamic Models In Bayesian Time Series Analysis And Forecasting. In Addition To Wide Ranging Updates To Central Material, The Second Edition Includes Many More Exercises And Covers New Topics At The Research And Application Frontiers Of Bayesian Forecastings. Introduction -- Introduction To The Dlm: The First-order Polynomial Model -- Introduction To The Dlm: The Regression Dlm -- The Dynamic Linear Model -- Univariate Time Series Dlm Theory -- Model Specification And Design -- Polynomial Trend Models -- Seasonal Models -- Regression, Autoregression, And Related Models -- Illustrations And Extensions Of Standard Dlms -- Intervention And Monitoring -- Multi-process Models -- Non-linear Dynamic Models: Analytic And Numerical Approximations -- Exponential Family Dynamic Models -- Simulation-based Methods In Dynamic Models -- Multivariate Modelling And Forecasting -- Distribution Theory And Linear Algebra. Bibliography -- Author Index -- Subject Index. Mike West, Jeff Harrison. Includes Bibliographical References (p. [652]-665) And Indexes. This Text Is Concerned With Bayesian Learning, Inference And Forecasting In Dynamic Environments. We Describe The Structure And Theory Of Classes Of Dynamic Models And Their Uses In Forecasting And Time Series Analysis. The Principles, Models And Methods Of Bayesian Forecasting And Time - Ries Analysis Have Been Developed Extensively During The Last Thirty Years. Thisdevelopmenthasinvolvedthoroughinvestigationofmathematicaland Statistical Aspects Of Forecasting Models And Related Techniques. With This Has Come Experience With Applications In A Variety Of Areas In Commercial, Industrial, Scienti?c, And Socio-economic ?elds. Much Of The Technical - Velopment Has Been Driven By The Needs Of Forecasting Practitioners And Applied Researchers. As A Result, There Now Exists A Relatively Complete Statistical And Mathematical Framework, Presented And Illustrated Here. In Writing And Revising This Book, Our Primary Goals Have Been To Present A Reasonably Comprehensive View Of Bayesian Ideas And Methods In M- Elling And Forecasting, Particularly To Provide A Solid Reference Source For Advanced University Students And Research Workers. This edition features revised, updated and additional material on the structure, theory and application of classes of dynamic models in Bayesian time series analysis and forecasting. This edition also contains more exercises and covers new topics at the research and application frontiers