This volume focuses on a time series model in Single Source of Error state space form, called 'ADAM' (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models to solve real life problems. Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) focuses on a time series model in Single Source of Error state space form, called "ADAM" (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models, explaining how a variety of instruments can be used to solve real life problems. At the moment, there is no other tool in R or Python that would be able to model both intermittent and regular demand, would support both ETS and ARIMA, work with explanatory variables, be able to deal with multiple seasonalities (e.g. for hourly demand data) and have a support for automatic selection of orders, components and variables and provide tools for diagnostics and further improvement of the estimated model. ADAM can do all of that in one and the same framework. Given the rising interest in forecasting, ADAM, being able to do all those things, is a useful tool for data scientists, business analysts and machine learning experts who work with time series, as well as any researchers working in the area of dynamic models.Key Features: It covers basics of forecasting, It discusses ETS and ARIMA models, It has chapters on extensions of ETS and ARIMA, including how to use explanatory variables and how to capture multiple frequencies, It discusses intermittent demand and scale models for ETS, ARIMA and regression, It covers diagnostics tools for ADAM and how to produce forecasts with it, It does all of that with examples in R.