Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM)

Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) is a book that demonstrates a holistic view to forecasting and time series analysis using dynamic models. It can model both intermittent and regular demand, support both ETS and ARIMA, work with explanatory variables, deal with multiple seasonalities, and have a support for automatic selection of orders, components, and variables. It is a useful tool for data scientists, business analysts, machine learning experts, and researchers working in the area of dynamic models.

Format: Hardback
Length: 466 pages
Publication date: 17 November 2023
Publisher: Taylor & Francis Ltd

Forecasting and Analytics with the Augmented Dynamic Adaptive Model (ADAM) is a comprehensive book that delves into the realm of time series modeling. It presents a time series model in Single Source of Error state space form, referred to as "ADAM" (Augmented Dynamic Adaptive Model). The book offers a holistic perspective on forecasting and time series analysis, employing dynamic models to address real-life problems. Currently, there is no other tool in R or Python capable of modeling both intermittent and regular demand, supporting both ETS and ARIMA, working with explanatory variables, handling multiple seasonalities (e.g., for hourly demand data), and offering automatic selection of orders, components, and variables. ADAM excels in all these aspects within a single framework.

Given the growing interest in forecasting, ADAM emerges as a valuable tool for data scientists, business analysts, machine learning experts, and researchers alike who work with time series. Its key features encompass the fundamentals of forecasting, extensive discussions on ETS and ARIMA models, chapters on extensions of ETS and ARIMA, including their application with explanatory variables and capturing multiple frequencies, comprehensive coverage of intermittent demand and scale models for ETS, ARIMA, and regression, and the inclusion of diagnostic tools for ADAM. Furthermore, the book provides practical guidance on producing forecasts using ADAM, accompanied by examples in R.

By leveraging the power of ADAM, practitioners can gain a deeper understanding of time series dynamics, develop more accurate forecasting models, and make informed decisions based on data. Its versatility and efficiency make it a must-have resource for anyone seeking to excel in the field of forecasting and time series analysis.

Weight: 884g
Dimension: 161 x 243 x 37 (mm)
ISBN-13: 9781032590370