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This book discusses the practical forecasting and analysis of time series. It addresses the question of how to analyze time series data: how to identify structure, how to explain observed behavior, how to model those structures and behaviours, and how to use the insights gained from the analysis to make informed forecasts.

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,...