Applied Bayesian Forecasting and Time Series Analysis

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CRC Press, Sep 1, 1994 - Mathematics - 480 pages
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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.

 

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Contents

Practical Modelling and Forecasting
3
Methodological Framework
13
Analysis of the DLM
29
1 Review of Distribution Theory
75
2 Classical Time Series Models
83
Turkey Chick Sales
91
Market Share
121
Marriages in Greece
147
Introduction to BATS
243
1 Files and Directories
255
Advanced Modelling
301
Modelling with Incomplete Data
341
Data Management
359
Chapter 14
373
Menu Descriptions
381
Index
405

Further Examples and Exercises
165
Installing BATS
235

Common terms and phrases

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