Provides the theories, methods, and tools necessary for forecasting and analysis of time series. Includes a complete theoretical development of the dynamic linear model, with each step demonstrated with analysis of real time series data, and explores aspects of time series, component decomposition, and model forms. Suitable for undergraduate and beginning graduate students in statistics, economics, engineering, and operations research. The accompanying disk contains BATS and data sets. Lacks a bibliography. Annotation copyright Book News, Inc. Portland, Or.
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.
