Applied Bayesian Forecasting and Time Series Analysis

Andy Pole

ISBN 10: 0367449382 / ISBN 13: 9780367449384

Editore: Chapman And Hall/CRC, 2019

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nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Codice articolo 9780367449384

Dati bibliografici

• Titolo: Applied Bayesian Forecasting and Time Series Analysis
• Editore: Chapman And Hall/CRC
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 0367449382
• ISBN 13: 9780367449384
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 234 L x 156 A x 23 P (millimetri) 234 millimetri di larghezza per 156 millimetri di altezza per 23 millimetri di profondità
• Peso: 649 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

Pole, Andy; West, Mike; Harrison, Jeff