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,

1. Introduction  2. Forecasts evaluation  3. Time series components and simple forecasting methods  4. Introduction to ETS  5. Pure additive ADAM ETS  6. Pure multiplicative ADAM ETS  7. General ADAM ETS model  8. Introduction to ARIMA  9. ADAM ARIMA  10. Explanatory variables in ADAM  11. Estimation of ADAM  12. Multiple frequencies in ADAM  13. Intermittent State Space Model  14. Model diagnostics  15. Model selection and combinations in ADAM  16. Handling uncertainty in ADAM  17. Scale model for ADAM  18. Forecasting with ADAM  19. Forecasting functions of the smooth package  20. What’s next?