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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 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.
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.
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.
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.
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