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Forecasting and analytics with the augmented dynamic adaptive model (ADAM) /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Forecasting and analytics with the augmented dynamic adaptive model (ADAM) /Ivan Svetunkov.
作者:	Svetunkov, Ivan,
出版者:	Boca Raton, FL :CRC Press,2024.
面頁冊數:	xxvii, 465 p. :ill. ;25 cm.
標題:	Box-Jenkins forecasting.
ISBN:	9781032590370 :

Forecasting and analytics with the augmented dynamic adaptive model (ADAM) /
Svetunkov, Ivan,

Forecasting and analytics with the augmented dynamic adaptive model (ADAM) /Ivan Svetunkov. - 1st ed. - Boca Raton, FL :CRC Press,2024. - xxvii, 465 p. :ill. ;25 cm.

Includes bibliographical references and index.

Forecasts evaluation -- Time series components and simple forecasting methods -- Introduction to ETS -- Pure additive ADAM ETS -- Pure multiplicative ADAM ETS -- General ADAM ETS model -- Introduction to ARIMA -- ADAM ARIMA -- Explanatory variables in ADAM -- Estimation of ADAM -- Multiple frequencies in ADAM -- Intermittent state space model -- Model diagnostics -- Model selection and combinations in ADAM -- Handling uncertainty in ADAM -- Scale model for ADAM -- Forecasting with ADAM -- Forecasting functions of the smooth package -- What's next?.

"Forecasting and Analytics with 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"--

ISBN: 9781032590370 :NT$3885

LCCN: 2023024208Subjects--Topical Terms:

184636
Box-Jenkins forecasting.

LC Class. No.: QA280 / .S94 2024

Dewey Class. No.: 511/.43

Forecasting and analytics with the augmented dynamic adaptive model (ADAM) /
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245 1 0 $a Forecasting and analytics with the augmented dynamic adaptive model (ADAM) / $c Ivan Svetunkov.
250 $a 1st ed.
260 $a Boca Raton, FL : $b CRC Press, $c 2024.
300 $a xxvii, 465 p. : $b ill. ; $c 25 cm.
504 $a Includes bibliographical references and index.
505 0 $a Forecasts evaluation -- Time series components and simple forecasting methods -- Introduction to ETS -- Pure additive ADAM ETS -- Pure multiplicative ADAM ETS -- General ADAM ETS model -- Introduction to ARIMA -- ADAM ARIMA -- Explanatory variables in ADAM -- Estimation of ADAM -- Multiple frequencies in ADAM -- Intermittent state space model -- Model diagnostics -- Model selection and combinations in ADAM -- Handling uncertainty in ADAM -- Scale model for ADAM -- Forecasting with ADAM -- Forecasting functions of the smooth package -- What's next?.
520 $a "Forecasting and Analytics with 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"-- $c Provided by publisher.
650 0 $a Box-Jenkins forecasting. $3 184636
650 0 $a Trend surface analysis. $3 1011722
650 0 $a Error analysis (Mathematics) $3 182680
650 0 $a Adaptive sampling (Statistics) $3 182283
650 0 $a Seasonal variations (Economics) $3 647619