Forecasting and Analytics with ADAM focuses on a time series model in Single Source of Error state space form, called â ADAMâ (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 ADAM focuses on a time series model in Single Source of Error state space form, called â ADAMâ (Augmented Dynamic Adaptive Model). The book demonstrates a holistic view to forecasting and time series analysis using dynamic models to solve real life problems.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Ivan Svetunkov is a Lecturer of Marketing Analytics at Lancaster University, UK and a Marketing Director of Centre for Marketing Analytics and Forecasting. He has PhD in Management Science from Lancaster University and a candidate degree in economics from Saint Petersburg State University of Economics and Finance, Russia. His areas of interests includes statistical methods of analytics and forecasting, focusing on demand forecasting in healthcare, supply chain and retail. He is a creator and a maintainer of several forecasting and analytics-related R packages, such as greybox, smooth and legion.
Inhaltsangabe
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?
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?
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