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.

part |2 pages

Part A: DYNAMIC BAYESIAN MODELLING Theory and Applications

chapter 1|10 pages

Practical Modelling and Forecasting

chapter 2|16 pages

Methodological Framework

chapter 3|46 pages

Analysis of the DLM

chapter 4|30 pages

Application. Turkey Chick Sales

chapter 5|26 pages

Application: Market Share

chapter 6|18 pages

Application: Marriages in Greece

chapter 7|68 pages

Further Examples and Exercises

part |2 pages


chapter 8|8 pages

Installing BATS

chapter 9|12 pages

Tutorials Introduction to BATS

chapter |4 pages

Appendix 9.1: Files and Directories

chapter 10|42 pages

Tutorial: Introduction to Modelling

chapter 11|40 pages

Tutorial: Advanced Modelling

chapter 12|18 pages

Tutorial: Modelling with Incomplete Data

chapter 13|12 pages

Tutorial: Data Management

part |2 pages


chapter 14|8 pages


chapter 15|24 pages

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