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Synopsis
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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About the Author
Pole, Andy; West, Mike; Harrison, Jeff
From the Back Cover
Applied Bayesian Forecasting and Time Series Analysis includes a complete theoretical development of the dynamic linear model, with each step demonstrated with analysis of real time series data. The result is a clear presentation of the Bayesian paradigm: quantified subjective judgments derived from selected models applied to time series observations.
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Applied Bayesian Forecasting and Time Series Analysis
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Applied Bayesian Forecasting and Time Series Analysis
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Applied Bayesian Forecasting and Time Series Analysis (Chapman & Hall/CRC Texts in Statistical Science)
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Applied Bayesian Forecasting and Time Series Analysis
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Applied Bayesian Forecasting and Time Series Analysis (Hardcover)
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Taylor & Francis Ltd, 1994
ISBN 10: 0412044013
ISBN 13: 9780412044014
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Hardcover. Condition: new. Hardcover. 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 book is about practical forecasting and analysis of time series. It describes how to analyse time series data, how to identify structure, how to explain observed behaviour, how to model structures and behaviours, and how to use insight gained from the analysis to make informed forecasts. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9780412044014
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