Description
Book SynopsisForecasting 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 anal
Table of Contents
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?
