Applied Bayesian Forecasting and Time Series Analysis (Chapman & Hall/CRC Texts in Statistical Science)

Pole, Andy; West, Mike; Harrison, Jeff

ISBN 10: 0412044013 / ISBN 13: 9780412044014

Published by Chapman and Hall/CRC, 1994

About this Item

Please read **** Minor wear to outside cover - my shelf location 2-c-8*. Seller Inventory # 190601006

Bibliographic Details

Title: Applied Bayesian Forecasting and Time Series...

Publisher: Chapman and Hall/CRC

Publication Date: 1994

Binding: Hardcover

Condition: As New

About this title

Synopsis:

Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Explore diverse aspects of time series, including how to identify, structure, explain observed behavior, model structures and behaviors, and interpret analyses to make informed forecasts Illustrate concepts such as component decomposition, fundamental model forms including trends and cycles, and practical modeling requirements for routine change and unusual events Conduct all analyses in the BATS computer programs, furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.

From the Back Cover:

Applied Bayesian Forecasting and Time Series Analysis includes a complete theoretical development of the dynamic linear model, with each step demonstrated with analysis of real time series data. The result is a clear presentation of the Bayesian paradigm: quantified subjective judgments derived from selected models applied to time series observations.