Bayesian Forecasting and Dynamic Models (Springer Series in Statistics) Review

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(More customer reviews)A Bayesian approach is a natural way to deal with time series data. You construct a model based on past data and prior information and use the model to predict future values in the series. When the new observations come in the model can be updated (model parameters reestimated) and forecasts can be updated. Most of the time series literature deals with the classical (frequentist) approach incluing the well-known book by Box and Jenkins on forecasting and control. This book provides a mathematically rigorous treament of time series modeling based on a Bayesian approach. Many common forecasting procedures including the Kalman filter are iterative algorithms that could be derived as solutions for forecasting based on a Bayesian model of the time series.
This is the best text available on this topic.

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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.

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

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(More customer reviews)This is the second text authored by West and Harrison on the Bayesian approach to time series. I have read and reviewed both of them. The other text by these authors provides a rigorous development of time series analysis using Bayesian methods. While that text is very good at helping the reader understand the theory and the whys and hows of implementing it, this text is very much a book that shows applications and how the Bayesian time series approach is used to produce forecasts based on this methodology. The authors are expert researchers on this topic and aside from these two books texts on Bayesian approaches to time series are not easy to find.

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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 andthe 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.

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